نشریه پژوهش های اقلیم شناسی
پیاپی 19-20 (پاییز و زمستان 1393)

Studies on rainfall distribution in catchments suffer from scattered distribution and limited number of measuring stations in the catchment area, there is no possibility for detailed analysis. Currently, numerical simulations are the best approach to determine real precipitation on a regular grid in an entire basin. At present, dynamical and physical governing equations of numerical models based on the latest numerical methods provide extensive and valuable information. . Hence, these models are used independently to evaluate occurrence and changes of various atmospheric parameters or effects of changes in one parameter on the others.
In this study, we have evaluated performance of different micro-physical and convection parameterization schemes of WRF model, to estimate precipitation in the Karoon river basin in Southwest Iran. The basin is a main water source of the country, and because of its large water reservoir, the region has been always interesting to researchers. Therefore, the region was selected for study. A mountainous region lies to North and northeast of the basin and the Khuzestan plain in South and West to the area.
Physics of the numerical Weather Research and Forecast (WRF) model, which is developed by different USA institutes, contains 5 categories of: (1) microphysics, (2) cumulus parameterization, (3) planetary boundary layer (PBL), (4) land-surface model, and (5) radiation. Since from its introduction, Operational use of the model has grown significantly. To have a better prediction, it is essential to find and apply the most appropriate configuration for the model in Iran.
The WSM3 is a simple-ice scheme, which predicts three categories of hydrometers: vapor, cloud water/ice, and rain/snow. The scheme computes ice processes efficiently, but not super-cooled water and gradual melting rates. The WSM5 scheme is similar to the WSM3 scheme and includes vapor, rain, snow, cloud ice, and cloud water in five different arrays. Thus, it keeps super-cooled water and gradual melting of snow falling from melting layer. The scheme is efficient in intermediate grids between the meso-scale and cloud-resolving grids.
In practice, the convection parameterization include following steps: Triggering (Determines occurrence/localization of convection), Cloud modeling (Determines vertical distribution of heating, moistening and momentum changes) and Closure (Determines overall amount of the energy conversion, convective precipitation=heat release). Types of convection schemes are based on moisture budgets, Adjustment and Mass-flux schemes. The Kain-Fritsch scheme utilizes a simple cloud model with moist updrafts and downdrafts, including effects of detrainment and entrainment, and a relative simple microphysics. In the Betts-Miller-Janjic scheme, deep convection profiles and the relaxation time are variable depending on the cloud efficiency and a non-dimensional parameter that characterizes the convective regime. The cloud efficiency depends on the entropy change, precipitation, and Cloud mean temperature. A requirement for the shallow convection moisture profile is that the entropy change should be small and non-negative.
Rainfall prediction is one of the important applications of numerical weather prediction models. Effects of different physical schemas and their combinations were studied in order to select best schemes for more accurate rain prediction, in the very important karoon basin catchment of Iran. To pursue this goal, a matrix of 6 WRF model configurations, were created, using combinations of different microphysical-convection schemes, and were run in two distinct domains with horizontal resolutions of 27km and 9km respectively, for four cases of: January 2004, March 2005 and 2007, and December 2009. In all runs, two different treatments of convection ( i.e., Kain-Fritsch(KF) and Betts-Miller-Janjic (BMJ)) and three different microphysical schemes ( i.e., WSM 3-class(3), WSM 5-class(5) and Ferrier(F)) were used. Also, FNL data from NCEP and observation data from IRIMO were used. Model results were compared with 6-hourly observed data of precipitation from 15 synoptic stations in the region. To evaluate prediction accuracy of different schemes, mean squared correlation coefficients between observation data and each combined convective-microphysics scheme was calculated.
Mean squared correlation coefficients between observation data and combination of convective-microphysical schemes for 9-km resolution were 0.888 for BMJF, 0.885 for BMJ5, 0.831 for BMJ3, 0.887 for KF3, 0.878 for KF5 and 0.871 for KFF. Therefore, results of the studied cases show no significant difference among the convective-microphysics configurations for the 9km resolution, which are in agreement with the results obtained by Jankov (2005) and Otkin (2008). So it seems that, at intermediate scales (about 9km), regardless of the sensitivity of the model to the microphysics and convection schemes, any of the compound schemes used in this study is acceptable. Therefore, for accurate analysis of schemes due to their differences in physical characteristics, it is better the Model be used in lower scales (for Convective cells). Also, application of preprocessing methods for Observational data assimilation, can detect behavior and sensitivity of the model regarding various schemes. On the other hand, if the convective scale is larger than the grid scale, the model directly resolves convection and precipitation. So, in this case, changing parameterization schemes have little effect on the size and intensity of rainfall. To realize the subject, a control run, without convection Parameterization schemes, should be compared with these model runs.
According to the results obtained the studied cases, there were no significant differences among the diverse convective-microphysics configurations for 9km resolution. Therefore, for an accurate schemes analysis due to differences in their physical characteristics and compare the results, it is better to use the Model in lower scales (higher resolutions) (for Convective cells).

The present paper introduces new drought indices with caption standardized precipitation evapotranspiration index (SPETI) that the first by Vicente-Serrano et al (2009) is presented. The SPETI is based on precipitation and temperature data, and it has the advantage of combining multiscalar character with the capacity to include the effects of temperature variability on drought assessment. The procedure to calculate the index is detailed and involves a climatic water balance, the accumulation of deficit/surplus at different time scales, and adjustment to a log-logistic probability distribution. Mathematically, the SPETI is similar to the standardized precipitation index (SPI), but it includes the role of temperature. Because the SPETI is based on a water balance, it can be compared to the self-calibrated Palmer drought severity index (sc-PDSI). Time series of the three indices were compared for a set of observatories with different climate characteristics, located in different parts of the world. Under global warming conditions, only the sc-PDSI and SPETI identified an increase in drought severity associated with higher water demand as a result of evapotranspiration. Relative to the sc-PDSI, the SPETI has the advantage of being multiscalar, which is crucial for drought analysis and monitoring.
Material and We describe here a simple multiscalar drought index (the SPETI) that combines precipitation and temperature data. The SPETI is very easy to calculate, and it is based on the original SPI calculation procedure. The SPETI uses the monthly (or weekly) difference between precipitation and PET. This represents a simple climatic water balance (Thornthwaite, 1948) that is calculated at different time scales to obtain the SPETI. The first step, the calculation of the PET, is difficult because of the involvement of numerous parameters, including surface temperature, air humidity, soil incoming radiation, water vapor pressure, and ground–atmosphere latent and sensible heat fluxes. Different methods have been proposed to indirectly estimate the PET from meteorological parameters measured at weather stations. We followed the simplest approach to calculate PET (Thornthwaite 1948), which has the advantage of only requiring data on monthly-mean temperature.
With a value for PET, the difference between the precipitation P and PET for the month i is calculated using which provides a simple measure of the water surplus or deficit for the analyzed month. Tsakiris et al. (2007) proposed the ratio of P to PET as a suitable parameter for obtaining a drought index that accounts for global warming processes. This approach has some shortcomings: the parameter is not defined when PET = 0 (which is common in many regions of the world during winter), and the P/PET quotient reduces dramatically the range of variability and deemphasizes the role of temperature in droughts. The calculated values are aggregated at different time scales, following the same procedure as that for the SPI.
For calculation of the SPI at different time scales, a probability distribution of the gamma family is used (the two-parameter gamma or three-parameter Pearson III distributions), because the frequencies of precipitation accumulated at different time scales are well modeled using these statistical distributions. Although the SPI can be calculated using a two-parameter distribution, such as the gamma distribution, a three-parameter distribution is needed to calculate the SPETI. In two-parameter distributions, the variable x has a lower boundary of zero whereas in three-parameter distributions, x can take values in the range where is the parameter of origin of the distribution; consequently, x can have negative values, which are common in D series.
The probability density function of a three-parameter log-logistic distributed variable is expressed as where , , and are scale, shape, and origin parameters, respectively, for D values in the range ().
Parameters of the log-logistic distribution can be obtained following different procedures. Among them, the L-moment procedure is the most robust and easy approach (Ahmad et al. 1988). When L moments are calculated, the parameters of the Pearson III distribution can be obtained following Singh et al. (1993).
The probability distribution function of the D series, according to the log-logistic distribution, is given by The F(x) values for the D series at different time scales adapt very well to the empirical F(x) values at the different observatories, independently of the climate characteristics and the time scale of the analysis. With F(x) the SPETI can easily be obtained as the standardized values of F(x).
Where and P is the probability of exceeding a determined D value, P =1- F(x). If P >0.5, then P is replaced by 1- P and the sign of the resultant SPETI is reversed. The constants are ,,,,and .
The average value of SPETI is 0, and the standard deviation is 1. The SPETI is a standardized variable, and it can therefore be compared with other SPETI values over time and space. An SPETI of 0 indicates a value corresponding to 50% of the cumulative probability of D, according to a log-logistic distribution.
The result of monthly calculated of SPETI and SPI for the 12-month time scale in Tabriz station for period 1951-2010 shows despite the little difference between two indicies but in last 16 year the SPETI shows drought better than the SPI so the number of drought years in SPETI is greater than SPI due to using temperature in SPETI, the severe wet( severe drought) years that SPI shows because of high temperature( low temperature) are adjusted and the opposite is also true. This means that when the SPI shows normal or near normal condition the SPETI shows drought (wet) years because of high (low) temperature.
The last 16 years is an example of situation due to rising temperature which the SPETI the more drought year than SPI. For example, when the SPI is in normal condition in 2002 and 2007 years, the SPI indicates the drought condition for those years.
According to the results the SPETI has more advantages than SPI for monitoring drought because of being multiscalar and using a few meteorological parameters for calculation. Last 16 years shows this difference because of decreasing rainfall and rising temperature.

The sun is the primary source of energy for Earth's atomosphere. Changes in the output energy of the sun and its surface temperature fluctuations can create fluctuations and changes in the Earth's atmosphere. Sunspot activity can affect the Earth's climate system at different time scales and ultimately causing fluctuations and climate change.
The main feature of sunspots is that those have fairly regular variability in 11-year cycle. When 11 year cycle of solar is maximum, there is intense solar activity. Therefore, total solar irradiance increased and the sun transforms energetic particle to space by the solar wind (Lean, 2001).
Much research on the relationship between annual and monthly precipitation and sunspot cycle is done (Fleer 1982; Seleshi et al. 1994; Pérez-Peraza et al.1999; Hiremath & Mandi. 2004; Bhattacharyya & Narasimha. 2004; Zhao et al. 2004; Souza Echer et al. 2008; Selvaraj et al. 2009; Ma et al. 2010; Roy & Haigh. 2012; and in IRAN: Jahanbakhsh & edalatdoost. 2008).
Wavelet analysis is a major development in the methods of data analysis in the last twenty years, in both research and applications. With concern over current climate changes and their attribution, the analysis of natural climate variability on relatively long timescales has attracted much attention in recent years. The wavelet transform of time series is a convolution with the local base functions or wavelets, which can be stretched and translated with a flexible resolution in both frequency and time. The wavelet transform decomposes a series into time-frequency space, enabling the identification of both the dominant modes of variability and the manner in which those modes vary with time. One of the wavelets which have both real and imaginary parts is the Morlet wavelet. This wavelet is the most commonly used complex wavelet in climate studies.
As with its Fourier counterpart, there is an inverse wavelet transform that allows the original signal to be recovered from its wavelet transform by integrating all scales and locations, a and b. If we limit the integration over a range of a scale rather than all of scale a, we can perform a basic filtering of the original signal (Addison. 2002).
In this study, was performed Spectral analysis of time series of temperature and precipitation using wavelet theory, to determine the effect of sunspots on the spectral behavior of temperature and precipitation in Iran, in a period of 43 years (1966-2009) in 41synoptic stations. The spectral separation of precipitation and temperature time series in the frequency band from 9 to 12 years using the inverse wavelet transform is done and compare it with time series of sunspots in different years. Then we calculate the correlations of these fluctuations at different stations.
The results show that the 11-year cycle of temperature and precipitation variability and its relation to sunspots, in any station is different. Fluctuations in temperature and precipitation with respect to solar cycle, in some of stations are inverse behavior and have similar behavior on others. In relation to rainfall, whatever move from lower latitudes to higher latitudes, the correlation between the sunspot cycle and the cycle of rainfall variability, changes from large negative values to positive values, therefore, at low latitudes the 11-year variability of precipitation and the number of sunspots has inverse behavior and has similar behavior at high latitudes. In relation to temperature, solar cycle in the South East, East and parts of central and southern coasts have more impact and from South East to the North West of the country, decrease the relationship between temperature and sunspot cycle.
Therefore, Wavelet analysis show different cycles with different intensity at climate time series such as temperature and precipitation. When the cycle is shorter, that is suggested a regional scale forcing and when that is longer, is related to a larger-scale atmospheric forcing.

Lake Urmia, the largest territorial lake, has a great role on climate balance in the region. In recent decades, agricultural and industrial activities, infrastructural and water resources development projects has been extremely propagated. Thereby, such activities have a remarkable impact on ecological conditionand water level of Lake Urmia. Although climate change also plays a great role on water level. Due to the importance of the issue, various studies have been carried out in recent years about the effect of climate change on water level changes.so in this paper attempt to simulate the fluctuations of the lake using artificial neural network approach and finally evaluate the effects of future climate change on lake levels.
Analyses in this research are done in 3 steps. In the first step, the climate change scenarios of temperature and rainfall resulted from a AOGCM model are generated under two scenarios A2 and B2 for the future period 201–2060. In the second, the outputs of this model are downscaled by the method of change-factor LARS downscaling. Thirdly, the Urmia lake level changes is modeled by the artificial neural network, and then the values are downscaled and introduced to the network to, finally, the lake water level values be computed at two emission scenarios.
2.1.Generation of climate change scenarios
To study climate change for the future periods, generation of climate scenarios is necessary, the most convincing way to generate which is utilizing the output of AOGCM models that are based on physics laws and mathematical formulae to be solved in a 3D network on the surface of the earth. To simulate the climate of the globe, the main climate processes- atmosphere, ocean, the earth’s surface, scale ice, and biosphere are simulated in tributary models separately, and then those of atmosphere and ocean are matched together to form AOGCM models (IPCC-TGCIA, 1999). In the present work, effects of climate change on lake levels have analyzed by outputs of HadCM3 model using A2 and B2 scenarios.
2.2.Downscaling outputs of HadCM3 Model
Local Down scaling: In this study, the method of “change factor downscaling” is used for local downscaling of AOGCM model. In this method the usual monthly ratios are obtained from historical series, and the climate variables simulated by AOGCM are derived from the cell in which the region under study is placed. First of all, the climate change scenarios for temperature and rainfall are generated. To compute the scenarios for each model, the “difference” values for temperature, and “ratio” for rainfall for long-term average per month in the period 2011–2030 and 2011-2060 as well as the simulated base period (1961-1990) is computed for each cell by the computational network.
Statistical downscaling: For comparison between the observed data and those from probability distribution and mean, the model LARS-WG uses Chi-square (χ2) test and t test, respectively. The tests are based on the assumption that the observed and simulated meteorological are the same. The test survey the null hypothesis to the effect that the two distributions or the two means are similar, so that the difference is not significant. In this research, the LARS model was calibrated by the observed data of minimum and maximum temperature as well as precipitation in the period 1961–1990. Afterwards, the LARS model was performed for climate change scenarios of temperature and precipitation in the region, and then the time series of temperature and precipitation for the period 2011–2060 was figured out. The results of downscaled temperature and precipitation for Urmia station is shown in Fig 1 and 2 respectively.
Fig1.: Downscaled temperature for Urmia station during 2011-2030
Fig 2.: Downscaled precipitation for Urmia station during 2011-2030
Simulation of climate change impacts on Urmia lake water level changes
An feed forward artificial neural network was used for simulation of lake level changes. To get the best result, various input models were defined and assessed some of which. Since the most important factors affecting water level of the lake are precipitation, entering discharges to the lake, temperature, and surface evaporation they are applied as inputs and biases in the network design. The final selected model to determine future lake level constitutes of inputs such as precipitation in month, total inflow in month and evaporation in month. Also the future total inflow to the lake was estimated by a feed forward network using monthly precipitation, monthly temperature and also number of Julian month.
In this research, 70 % of data of last period 1961–1980 was dedicated to the validation
stage, and the remaining 30 % for the validation stage 1980–1990. The Fig 3 shows the simulated monthly inflow to lake and observed flow during validation period.
Fig3. Simulated and observed inflow during validation period.
Simulation of monthly inflow to the Urmia lake for the future period and comparison with the observed period indicates the decline of average runoff into the lake Moreover, the results say that the changes of the modeled runoff in the humid period (May to April) are more than the dry period (November to May). Also, the results certify a decrease in the average future runoff in scenario A2 is more than scenario B1 relative to the base period. Also the simulated water level of Urmia lake shows that the lake level will decrease over the coming years and this decreasing are more than double in A2 scenario as compared to B2 scenario as shown in Fig 4.

In recent years dust storms as one of the most damaging environmental phenomena has engulfed over large parts of Iran and many human activities in the region have been affected (Khoshkish et al., 2011; Azizi et al., 2011). Dust storm is one of the environmental-climate disasters that its occurrence causing air traffic, prevalence of respiratory, heart disease and damage to agronomy (Rasoli et al., 2010). There is documentary evidence that the mineral dust aerosols affected on cloud formation and amount of precipitation and even reduces the acidity of precipitation (Ichoku et al., 2004). Intensity of dust storm determinate by survival, spatial distribution and visual reduction, and depending on continuity divided to short term (up to half an hour) and long-term (several hours or one day) (Lashkari et al., 2008). Due to consequence dust storm occurrence and its role in the life and human activity, many studies have been conducted on this event (Merrifield et al., 2013; Carnevale et al., 2012; Meloni et al., 2008). In some studies, dust frequency, their trend in the long term period and statistical analysis was investigated. But among them, climate studies more inspected synoptic analysis. Natsagdorj et al (2003) studied dust storms in Mongolia and showed that dust storms often occur in the Gobi desert and most occurrences are observed in the spring. Engelstadler (2001) investigated dust storm frequencies and their relationships to land surface conditions. Orvlosky et al (2004) analyzed spatial distribution, frequency, seasonality and diurnal variation of the dust storms in Turkmenistan and show that the highest mean annual frequency of such storms is observed in the spring in the sandy Central Karakum Desert. Zolfaghari and Abedzade (2004) investigated dust storms synoptic analysis in west Iran. According to their results maximal duration of dust storms is recorded in June and minimum in December. As previous studies have shown that the occurrence of these storms has increased in recent years in the West of Iran. Thus, Synoptic study of this event properties could be the present of their formation. In this study, it is assumed relatively deep down on Syria deserts caused to create the dust in Sanandaj. To this purpose the main synoptic patterns of sea level pressure and thickness 500-1000 hectopascal influencing the occurrence of dust storms in Sanandaj city were studied in 2009 to 2012.
Data and In this paper, environmental and circulatory database were used to be analyzed synoptic dust days. Daily records of dust storm occurrence during 2009-2012 by the Department of Environment Kurdistan and atmospheric parameter (sea level pressure 500-hPa and geopotential height 500-100-hPa) were utilized for synoptic analysis. In order to close study of dust storm, aerosol data were divided to two groups consist of days without dust storms (particular matter less than 200 µg.m-3) and dust storm days (concentrations of PM10 ranging 200-3000 µg.m-3). According to dust storms creation zone located in an area outside of Iran, we selected extensive area (0-60o E and 0-70o N). Finally the dust days database (94 days) was formed as a 94*783 matrix in MATLAB software. Then, cluster analysis was performed using Ward Linkage method to determinate sea level pressure and geopotential height pattern. At last, selected one day as representative dust days and its circulation patterns in Surfer software was plotted.
Sea surface level investigation Results showed that the cluster analysis upon sea surface pressure data represent three patterns. In the first pattern, located low pressure on Persian Gulf and its landing on Syria and Iraq desert due to dust storm in west of Iran. In the other hand, on the west Russia high pressure situated that cause air flowing from low latitude to high latitude. In the second pattern, the position cyclone on Iraq makes dust transportation to the study region. This pattern included maximum dust days. Climate condition dominated on third pattern is different from other patterns.
Geopotntial height 500-1000 hPa surveying The obtained results of cluster analysis upon 500 hPa represent three patterns exist. Mediterranean high trough in the first and third pattern, black sea low pressure and Saudi Arabia high in the second pattern are reasons for dust storm occurrence.
Dust storm in Sanandaj mainly occurs in spring, especially in July. In general, dust occurs when the strong pressure gradient is formed over dry source regions between Syria, Iraq’s desert and Iran, giving rise to strong wind conditions for the lifting of dust particles. In general, existence of landing deeply on easternmost Mediterranean sea, spreads air from arid regions (deserts of Libya, Egypt, northern Saudi Arabia and Iraq) toward Iran, and high trough Arabia have important role in the creating dust storms in southern, south-western and west of Iran ( particularly Sanandaj). Dust storms in Sanandaj mainly occur in warm half of the year. These events have increasing trend since late winter and reaches a peak in July.

1- On account of continuation nature and being quasi-stationary of blocking events, alternation in their abundance and duration can have a major effect on climatic conditions such as temperature and monthly and seasonal precipitation. The occurrence of very cold temperatures can be related to the blocking occurrence; a very cold temperature in winter (Europe 2009-2010) is connected to the blocking event and negative phase NAO. In our country many studies have been carried out on surveying the synoptic patterns of cold temperatures. In all these studies apart from two cases, the relationship of the blocking model with cold temperatures have not been considered directly, but so far have not been done a comprehensive and long-term research from the role of blocking models on country’s temperature. In this research, the role of blocking on Iran’s temperature in cold season have been investigated in quantity way in order to use a long statistical period in a climatology scale, All the happenings be cleared and on the other hand, the double effects of the blocking event that lead to a temperature less and more than normal become distinct and analyzed and its prevailing models be used in order to necessary per-knowledge and prediction; because reconnaissance and detection of the blocking happening can help to accuracy and carefulness of short-term prediction models. Iran according to its location has most abundance of the blocking event in this cold season, so in this research also only have been analyzed and detected the effective blocking patterns on temperature in these two seasons (fall and winter).
2-Data and Data related to geo-potential height of 500 hPa level in regard to two dimensions index of visible blocking, daily for cold season in year for a 65-year period from 1948 to 2013 in a 2/5 *2/5 network that is appropriate for studying the large-scale occurrences like the blocking was extracted from site NCEP-NCAR for limit 0 to 90 degrees northern latitude and 90 degrees western to100 degrees eastern longitude. Then the investigated zone was bounded according to the knowledge of the limit and the origin of the effective blocking on Iran’ weather from -40 degrees western to 100 degrees eastern longitude and 20 to 80 degrees northern latitude. To better revealing the effect of the blocking on temperature, was considered the maps of analogous temperature with each model in a smaller limit in longitude 20 to 70 degrees eastern and latitude 20 to 50 degrees northern.
The Blocking happening conditions were programmed based on two dimensions in software MAT LAB and all the 5-day events and more were extracted. Then in order to decrease the data and their classification was used an analysis way of main ingredients based on ornament S. Then using sorting in K-means way, the prevailing circulatory models of the blocking were extracted that totally 7 circulatory patterns were attained. Among these 7 models, 2 models were identified as effective blocking patterns on temperature that was including omega and double polar pattern. The synoptic Conditions each of the models on the level of ground and 500 hPa level were analyzed. The Temperature conditions on the level of ground were investigated for all the models, and for each of the patterns, an event has been showed on 500 hPa level daily. In this manner, the synoptic models of effective blocking happenings on Iran’ weather were identified to increasing accuracy and carefulness of the temperature predictions.
3- Atmospheric blocking with regard to its being quasi-stationary and large-scale nature can effect on the level of ground and with respect to the abundance of its happening can also have effect on monthly and seasonal temperature. In general the blocking event has a double-effect on temperature and can lead to a temperature less than normal (cold season) and more than normal (warm season) in the zone.
The Blocking is a large-scale complication that its different parts have various synoptic events. This variation in the blocking models of omega kind is more; the zones that are located under influence of forward part, because of falling cold weather from high latitudes face with temperatures less than normal that according to blocking happening severity and its continuity especially in cold season lead to frigidity waves with different severities. Under influence of the zones of ridge axis benefit from a temperature more than normal which this subject has a special importance in cold season for continental zones of high latitude causes a temperature modification for several days and even several weeks in the zones. Two patterns of effective blocking on temperature consists omega pattern: including two sub-patterns in front of ridge axis and ridge sub-axis and Splite pattern model.
4- The Blocking is a large-scale phenomenon with different models that each of its models also has different synoptic conditions. In cold season of year the blocking patterns that are effective on temperature are divided to two general patterns of omega and double-polar. The omega model has two subordinate patterns; one of them is the omega model in front of ridge axis which its under influence zones are together with cold temperatures and less than normal and a low-pressure system on the level of ground, but in the omega model, under influence zones of the model below ridge axis have a temperature more than normal and low-pressure on the level of ground. In the second pattern, the zones which were in front of above ridge with a high height on account of falling air from high latitudes were put under less than normal temperature happenings.
Ineffective blocking systems on falling, the block event and falling changes in terms of chronological have a harmony together, but studying relationship of the blocking models with the temperature on the level of ground showed that the events of the temperatures of more and less than normal resulting from the blocking event is together with a one-day chronological delay or less on the level of ground.
In general, studying the effective blocking models on the temperatures of less than normal showed that temperature decrease in the model in front of ridge axis of omega kind is usually more than the above high-height forward ridge in double-polar pattern. On grounds of blocking’s being quasi-stationary and continuation nature, the characteristics of resulting from it, there will not have a noticeable alternation in a zone for several days and sometimes several weeks; as a result, the knowledge of its various synoptic models and the different effects of resulting from them on ground can help to accuracy and carefulness of the short-term prediction models and temperature conditions in a way become predicted with a more confident for next several days.

Study of the physical characteristics of cloud with the aim of estimate of precipitation occurence helps to study the possibility of performing cloud seeding project in the appropriate time and place and reduce errors in making decisions in execution of increased precipitation operations and thus reduce the costs of projects. One of the best options to achieve physical and microphysical properties of clouds, especially in the absence of weather radar data, is the use of meteorological satellites products and data, such as MODIS.
MODIS high spectral resolution with 36 spectral bands and also installed sensors for cloud studies will enable researchers to identify different types of clouds among which 26 bands are allocated for purposes of investigation of atmospheric characteristics such as cloud cover, atmospheric profiles, characteristics of aerosols and total precipitable water and cloud properties.
Also, the findings of Seemann and his colleagues in MODIS high spatial resolution imagery (1 km and less) makes it very suitable for meteorological applications.
The purpose of this research is the use of cloud physics level-2 products (mode 06) of MODIS to identify and separate raining from non-raining cloud regions in order to increase accuracy and reduce operation costs of precipitation enhancement project and to investigate the precipitation conditions and forecast using cloud physical and microphysical characteristics. Due to lack of extensive coverage in weather radar and airborne laboratory in Iran, inevitably must do some innovative methods using available satellite data to detect the position of the rain clouds in the broad and diverse geographic extent in the shortest time.
Rosenfeld et al., (1994) investigated Relationship between precipitation and effective particle diameter in the top of cloud and showed that in clouds that the average of effective droplets radius is over 14 microns, the probability of precipitation is very high so that the results mentioned above are also used in this study.
On the other hand the results of the study conducted by Nauss and Kokhanovsky (2006) about a description of precipitation using cloud properties derived from optical satellite data for mid latitude front clouds were used.
Therefore, this article attempts to separate the raining and non-raining clouds using the results of research conducted, also the optical depth of clouds over a maximum of 40 units from 60 units with a high probability of precipitation were analyzed. On indirect or remote sensing is used physical characteristics of clouds for detection and prediction of precipitation. Present study was conducted in February 2009 in the Fars province. In this month the most cloud seeding flights of the year 2009 was performed (13 flights in 8 days cloud seeding operations) and local weather radar information is lacking.
To do a research on accuracy of flights during this time, in addition to MODIS satellite data including Physics and cloud microphysics products obtained through the NASA Web site, required weather maps produced by NOAA website (reanalysis maps from NCEP-NCAR) were used. Timely implementation of cloud seeding operations requires instantaneous information of weather conditions and cloud physics in the area. So that the country has access to MODIS sensor data through satellite receivers, it is possible to use these products for operational purposes and cloud seeding. For research purposes of available data of MODIS products on the NASA Website can also be used. MOD06 products which contains information on the characteristics of the cloud such as cloud phase, cloud top temperature, cloud top pressure, effective droplets diameter in the top of cloud and cloud optical depth or optical thickness have been used in the MOD06 algorithms and the results of it.
Results and Due to the large number of days and performed cloud seeding flights on February 2009 and also pictures and maps of flights, was decided to discuss as an example, two different and chosen days regarding to appropriate cloud systems or inappropriate for seeding is discussed. Therefore, the general conditions for cloud seeding operation is available if these conditions are occurred simultaneously, optical depth is greater than 35 units, cloud top temperature more than - 30 ° C, height of top of middle clouds is higher than 500 mb, diameter of inside middle cloud droplets is more than 19 mm and cloud fraction is as a unit in designed area.
Otherwise if dont establish two or more of the criteria listed, the cloud seeding operation should be avoided .This study determined if cloud seeding project in Fars province on February 2009 was used products from MOD06, the number of cloud seeding operation days can be reduced from 8 of cloudy day (in uncertain conditions of existence of appropriate measures for cloud seeding) to 3 useful and rainy days and consequently can be avoided of 6 completely ineffective cloud seeding flights.
So, in addition to reduce costs the possibility of increasing project accuracy and using of other seeding opportunities in other talented areas of the country was provided.

To monitoring the spatial snow cover, by using MODIS images (Aqua and Terra satellites), during the cold season (October to March) in the years 2005 to 2012 (9 years). These images, with daily intervals and spatial resolution of 250 meter were studied for 9-year. Snow surface zones in the plains of Guilan observed special triangular pattern that approximately matches the Sefidrud River Delta. To identify snow-covered areas, the snow area index (NDSI) was applied. Due to low reflectance of snow in the infrared bands and high reflectance in the visible bands, this indicator can be very useful in detecting snow cover from other phenomena. Using GIS software and algorithms to detect snow Guilan plain snow zones were identified. Results were extracted from the digital map. Then, this layer over layed on digital elevation map(DEM) of the area; the spatial pattern of snow area was prepared.
Data and To investigate the mechanism of pattern formation in Delta Snow, 6 events which lead the snowfall in delta form were selected, in the plains of Guilan during 8 year, was selected. Then, Daily and 6-hour maps of pressure, temperature, geopotential height, vertical velocity, zonal and meridional wind components fields from NCEP/NCAR data over a region consist of Iran with 2.5º×2.5 º horizontal resolutions were analyzed. To avoid prolongation of paper, system in March 2012 and the results obtained are presented in detail.
To evaluate reason of this process and simulate more details on a smaller scale ,weather and research forecast(WRF) model was used. model on Guillan, with three horizontal resolutions of 27, 9 and 3 km and the three-hour time step, was run. 10-meter wind, 2-meter temperature, relative humidity and cross section of dynamic parameters such as relative vorticity and vertical velocity were investigated. Study of the synoptic structure of this systems leads to the formation of this phenomenon, show the origin of this system is the anticyclone (high) with a central pressure greater than 1035 that is formed on west of Europe and East of Atlantic ocean (Azores high pressure) and With the move to the East and are expanded over the Middle Caspian. Anticyclone has spread to the southern shores of the Caspian and clockwise circulation in the lower troposphere cause flows north and northwest, from the Caspian Sea towards the coast. Surface cyclone in the center of Iran and anticyclone in north of Iran, increasing pressure gradient and wind speed on the southwest coast of the Caspian Sea. In the early hours of arrival system to Iran, in 850 hPa, ridge on the southern latitude and trough on the northern latitude (Parts of northern Russia) and anti-clockwise circulation due to the trough on the northwest of Iran, South and southwest cold flow, moves from the Iranian plateau to higher latitudes.
Based on WRF model output, 2-meter temperature and 10-meter wind pattern, shows forming a strong north and northwest flow in the southwestern margin of the Caspian Sea that correspond to the surface maps. South of the Alborz mountain range in height from sea level to adapt to the higher level of 900 hPa, therfore trough and south and southwestern currents is overcome, but Manjil gap is only natural passage and cross region between the northern and southern currents of Guilan plains and southern Alborz. wind Cross section shows that above latitude 37º, in the lower levels of the troposphere, according to surface anticyclone dominance and anti-clockwise circulation of high-pressure, as north and northwest currents are observed mostly. But, in the middle and upper troposphere, the western wave and trough, wind direction along the Manjil meridian is southwest. In other words, the vertical wind shear and instability in the layer between 700 and 800 hPa levels occur. Furthermore, Manjil gap(latitude 36/7),is place that horizontal wind shear between the two sides of the Alborz Mountains were seen. 2-meter temperature pattern and cross sections of temperature, temperature gradient (temperature Changes from 5 to 8 degrees Celsius in the aerial distance less than 20 km) between the southern high lands of Alborz (lower temperature) and plains of Guilan (higher temperature) clearly shows. Before the establishment of the high-pressure core over the Caspian Sea, tropospheric cooling of the lower layers is not enough for snowfall in the plains of Guilan. Cold air, due to crossing thermal trough of middle levels troposphere from the Iranian plateau and Alborz Mountain (have greater height and lower surface temperature compared with Delta Sefidrud) makes the cold currents of the Iranian plateau in the White River Valley prevail and happen snowfall in the plains of Guilan. Based on the model output at 3 km spatial scale, cross section and horizontal wind shear and substantially horizontal temperature gradient shows formation of local front according to Manjil gap. Regardless of the physical and dynamics mechanisms of snowfall, the topography of the region and Manjil gap in the Alborz Mountains is the only way to Influence of the cold plateau air to Guilan plain that corresponded with Delta pattern formation. Following the establishment of a cold anticyclone over central Caspian and strengthen cooling the lower of tropospheric layers over central Caspian, temperatures on the plains of Guilan and the temperature contrast between the Guilan plain and the southern Alborz, (Qazvin plain) decreases. Furthermore, located southwestern margin of the Caspian Sea coast in the Western branch 850 hPa trough, that accompanied with strong northern currents along the western and central Alborz, lead to dominance the northern winds to the south of the Alborz and spread to 700 hPa. Cross section of wind has also confirmed that under these conditions increases instability and vertical wind shear.