نشریه پژوهش های اقلیم شناسی
پیاپی 3-4 (پاییز و زمستان 1389)

IntroductionPrecipitation is a variable and irregular over most part of Iran. Therefore it is a very important factor in planning procedures throughout the country. The spatial variation of the annual precipitation varies from 1800 millimeters in Anzali to less than 50 millimeters in the arid and semi-arid deserts of the southeast. Its temporal variability is much higher than the spatial variations. More than 90 percent of the daily rainfall comes in low and moderate intensities. But sometimes very intensive and torrential rains occur, especially in the arid climate of the country. These extreme rainfalls are characteristics of the dry parts of the country such as the southern coastal areas. Humidity of the Persian Gulf and Oman Sea and the deep and intensive westerly cause torrential rains during the cold seasons. Sometimes the rainfall hit more than 80 percent of the annual precipitation. Therefore the very important role of it in the management and development of the region is not deniable. The results and understanding of these patterns will help the authorities and planning managers. The most important factor resulting the torrential rainfalls is the pressure patterns of the westerly. This paper investigates concepts of the westerly pressure systems in very intensive daily rainfalls of the southeast of Iran. Data and Methods In order to understand the pressure patterns of these heavy rains, the daily rainfall data of 25 weather stations of the southeast region of Iran were obtained from I.R of Iran Meteorological Organization during the 1997-2007 period. The heaviest daily rainfall is a day having at least 100 millimeters rainfall in one third of the stations under investigation, for sure. In the analysis of the cold season, January 6, 2008 was selected as

IntroductionPrecipitation systems have a main role in water supply, so identifying their characteristics such as understanding their formation, water resources and locating their activities are really important. The interaction of Sea Surface Temperature and precipitation in different regions of the Earth has been emphasized by many researchers. Studies show that high-latitude jet stream is an important factor in developing mid-latitude systems. The purpose of this research is to understand the synoptically structure of the extreme precipitation pattern in the North West of Iran and can be used as a diagnostic factor in the weather forecasting.Materials and MethodsPrecipitation data source are obtained from I.R of Iran Meteorological Organization. For investigating synoptically pattern and anomaly for extreme precipitation in the North-West of Iran, mean sea level pressure, 500-hpa geopotential height, 300-hpa jet stream from data analysis of National Centers for Environmental Prediction / National Center for Atmospheric Research (NCEP / NCAR) are considered by using advanced very high resolution radiometer (AVHRR) data for analyzing sea surface temperature. Some parameters such as Mean sea level pressure and 500-hpa geo-potential height with their anomaly, 300-hpa level of jet stream and water temperature were investigated and analyzed.the day with the highest records of rainfall during the study period. The 6-hourly upper level atmospheric variables for the days 4 to 6 of January, 2008 were extracted from the NCEP reanalysis data web site. These variables include: sea level pressure maps, geo-potential height of upper level pressure levels, the U and V components of upper level winds, vertical motion (Omega), Jet stream wind speed, and air temperature. These upper level data were obtained for a greater window of 30° W to 80° E and 0° to 70°N.Results and conclusionThe daily rainfall of January 6, 2008, was the heaviest daily rainfall ever recorded in the southwestern part of the country. The amount of rainfall ranged from 3 millimeters in the most eastern part to 50+ millimeters in the most southern part of the region. The analysis of the weather maps of the surface, 850, 500, and 300 hPa. levels revealed the following results. During the pre- rain days the region was under the stable upper level conditions; the Siberian High had been spread over the region on the surface and the upper westerly ridge and was established at the 500 hPa level. On the rainy day of January 6, 2008, the surface high pressure center has been retreated. On the upper level a deep westerly trough has moved over the area and had created very unstable conditions. On 850 hPa level the study region no humid winds recorded. But during the rainy day the wind from west and over the Persian Gulf added the required moisture. The instability indices of the atmosphere indicated strong instability on January 6, 2008. The wind maps showed the intrusion of the Persian Gulf moisture to the region from west and south. Another important factor was the presence of the westerly jet core over the region which accelerated the uplift of moist air. In brief, the southward penetration of a deep westerly trough at the upper atmosphere, intensive instability of the atmosphere, and the intrusion of moisture from the nearby water bodies caused the heavy rain of January 6, 2008. The results confirmed the previous findings that when a deep trough penetrates to the south of Iran lifts the moist air of the water bodies of the region and causes very intensive torrential rains.

IntroductionPlanning and management of water resources in Iran are affected by tempo-spatial distribution of rainfall. Therefore, study of precipitation can be a vital variable for evaluating water resources in different parts of Iran. Since climatic data are increasing in bulk study the individual places takes, therefore using of multivariate regionalization methods are inevitable. Recently, multivariate methods of cluster analysis has been used widely in climatic regionalization and it provides desirable results. Another method of multivariate is polynomial analysis that is less frequently used. In the latter if a number of classes are used the result of regionalization can be tested. In present study precipitation of Northwest of Iran is classified using eight characteristics of precipitation using cluster analysis and were tested by discriminant analysis methods.Materials and MethodsFor this study rainfall profile of 260 stations Northwest are analyzed. After arranging the required data, 3360 monthly maps from elements have been made by the kriging interpolation method, after removal of outside cells, 5374 cells remain within the study area. Data matrix for the regionalization was obtained by the mentioned maps in 92 column and 5374 row. Cluster analysis used for classification. So that the distance of the cell obtained by Euclidean distance coefficient and precipitation groups linked by 'Ward' linkage method. To obtain the optimal number of classes precipitation average method and the comparison process of elements have been used. Finally, four groups of precipitation were obtained in Northwest of Iran.In order to test the accuracy of the results of cluster analysis, discriminant analysis was used. The results of discriminant analysis accepted qualifications of cluster analysis in classification. So that results obtained from discriminant analysis in 97.6 percent of the regional area conform to the results of cluster analysis. Results and DiscussionThe results indicate the interaction between tropical and polar jet streams caused cyclogenesis in the North East of the Mediterranean Sea and also they determined that the anomaly of mean sea level pressure, 500-hpa geo-potential height and sea surface temperature can be used as a tool for predicting extreme precipitation. In the case with heavy precipitation, Mean sea level pressure and 500-hpa geo-potential height, respectively 10 mb and 140 meters are less than the average. In the light precipitation case, these records are 5 and 60 respectively. Anomaly of sea surface temperature in extreme precipitation shows that the Mediterranean Sea, Black Sea, Oman Sea and Indian Ocean are much warmer than average, which causes more evaporation and proper injection of moisture to the region.ConclusionTo determine the structure of synoptically pattern of extreme precipitation in North West of Iran, some meteorological parameters such as mean sea level pressure, 500-hpa geo-potential height, 300-hpa jet stream and sea surface temperature, caused heavy rainfall in March in North West of Iran, are studied in this case study. Since the fluctuations are more revealed in maximum and minimum precipitation conditions, the extreme conditions are investigated. 14th March 2009 and 4th March 2005 are chosen as a maximum and minimum precipitation respectively. The data of NCEP / NCAR were the base for reviewing synoptically parameters and the AVHRR data were used to monitor the sea level temperature. The results show that the interaction between tropical and polar jet streams are generating cyclogenesis in the North East of the Mediterranean Sea and also they determined that the anomaly of mean sea level pressure, 500-hpa geo-potential height and sea surface temperature can be used as a tool for predicting extreme precipitation.

IntroductionIn this study, Soil and Water Assessment Tool (SWAT) was used to evaluate the effects of climate change on river flow in the Ghare sou watershed. Karkheh Basin is located in the west of Iran with area of 50764 km2. It is located in the middle parts and southwest regions of Zagros Mountains between 46° 06´ and 49° 10´ of east longitude also between 30° 58´ and 34° 56´ north longitude. Ghare Sou is a sub basin of Karkheh in northwestern of Karkheh basin and west of Iran. Its area is 5354 km2 with a maximum and minimum height of the 3346m and 1180 m respectively. Average of annual rainfall varies between 300mm to 800 mm.At present, various methods for generating climate scenarios are used for future periods. These methods include: producing synthetic scenarios, using past climate parameters and using Atmosphere Ocean General Circulation Model (AOGCM).Current most reliable tools for producing climate scenarios are three dimensional coupled models of ocean-atmosphere general circulation (Wilby and Harris 2006, Mitchell 2003). AOGCM models are based on physical laws and mathematical relationships. These relationships solve in a three dimensional network of the earth surface. AOGMCM is a valid tool for predicting climate change and producing hydrological model inputs. Recently, some studies have been done about effects of climate change on runoff. These studies combined the outputs of general circulation models and hydrological models. Predictions by AOGCM is the first attempt to assess the effect of climate change on hydrology at the basin scale.Results and discussionTo assess water balance of the basin SWAT model was used. Physical-based and distributed parameters were developed to prediction of land use changes and management practices in the large and complicated basins Differences in the results of these methods were in border cells of groups and for appointment to own groups of these cells, their subsequent probability were used.Results and DiscussionNorthwest precipitation groups are as follows:1.The first group (low concentrated precipitation region with moderate snowfall): The first group has 306.5 mm of total annual rainfall and 33559.9 of area. It is located in two separate parts of the South East of study area and near of Oroumieh Lake.2.The second group (Moderate precipitation region with tendency to low distributed precipitation and much snowfall): the second group has 348.3 mm of total annual rainfall and 62847. 65 of area. It is located in the northern half of the study area. This group is allocated more than half the area of the Northwest.3.The third group (much concentrated precipitation region with low snowfall): the third group has 631.2 mm of total annual rainfall and 4972.27 of area and it is located in the Southwest of study area.4.The fourth group (moderate precipitation region with tendency to much distributed precipitation and moderate snowfall): The fourth group has 436.8 mm of total annual precipitation and 17290.22 of area. It is located in two separate parts in the south to the west and the central part of the study area.In comparing the groups some differences in the amounts of precipitation, distribution of precipitation, snow days and start of rainfall season in groups are detected. Amount of annual rainfall in high rainfall group is more than double of low rainfall group. Linear functions obtained from discriminant analysis determined the boundary between these groups.

Introduction The aerosols present in the atmosphere that consist of suspended solid and liquid particles and have natural and human origin, are the main causes of air pollution and consequently are the basis for creation and/or exacerbation of heart cardiovascular and respiratory. In recent years, the need for monitoring and control of air pollutants in wide scale and with an acceptable speed and accuracy is felt. The most important advances in satellite remote sensing is the ability to retrieve characteristics of particulate matter's over the land through use of MODIS sensor on Terra and Aqua platforms. Due to the availability of at least one MODIS images per day from our country, it is possible to estimate the suspended particles’ concentration in the atmosphere above each pixel. Materials and methods 20 stations in citywide record pollutants such as Carbon Monoxide, Nitrogen Dioxide, Sulfur Dioxide, concentration of Aerosol less than 10 micron size and lead concentration on the hourly based intervals. In this research MODIS level1 and MODIS level2 images were used. These data contains radiance and reflectance at top of atmosphere all coordinated, calibrated and atmospherically corrected. These data are available under names MOD02QKM, MOD02HKM, and MOD021KM with spatial resolution of 250, 500 and 1000 meters respectively. Along with these images, pollutant data collected in pollution surveying stations were used for quantitative and qualitative analysis of suspended particles in wide scales. (Verbeeten and Barendregt, 2007). SWAT model was developed to predict the effects of land management activities on water, sedimentation and chemical-agricultural agents with a variety of soil, land cover and management conditions in the large-term period. This model is a physical model. This model uses specific information about air, soil, topography, vegetation and land cover in the basin instead of using the equations to describe the relationships between input and output.Sub basins are divided to hydrological response parts (HRUs). Several parameters for model calibration have been selected. All parameters considering the river flow calibration have been adjusted by using of hydrometric station data of Ghara Baghestan. These parameters were selected according to previous researches in the field of calibration of SWAT model (Kati et al., 2005).Model calibration has been done with correlation function value, simulation of monthly flow, Nush-Sutcliffe (Ens), average of monthly flow and variance of monthly flow between 1992-1996. Model assessment have been carried out between 1998-2000Considering pronounced effect of temperature and precipitation variables on the water resource systems, agriculture, environment and etc., basic studies on the climate change effects is mainly on these two variables. As it can be seen, average of long-term monthly temperature increase in the period of 204-2069 the increasing of average annual is 2.6°C, maximum decrease is 4.5 °C in June and minimum decrease is 1°C in December. In season comparison, summer has maximum increasing in the temperature than other seasons. This increasing is 4°C, while average of winter temperature increase is 1°C.ConclusionIn this study the effect of climate change on the outflow of Ghare Sou basin in the period of 2040-2069 with HadCM3 model, one of the AOGCM model under A2 Scenario, has been analyzed. Continuous simulation model of runoff was applied to study hydrology of the basin under climate change conditions.The results of temperature and precipitation scenarios of HadCM3, from forth IPCC report, and simulated time series model with SWAT (rainfall-runoff model) showed that climate and hydrological parameters of Ghare sou have significant changes in the future.

Introduction In absence of long-term time series of meteorological variables required for different type of studies, simulation models of these variables, generally known as weather generators (WGs), have been developed in recently. Taking into account the significant climatic variations defined in climate change scenarios, it seems necessary to define these changes upon simulated series. Since conventional weather generators do not have such a capability, new methods should be taken into consideration. Models that have been used in empirical downscaling studies include multiple linear regression, canonical correlation analysis, nearest neighbor and artificial neural network approaches. These models can account for variability in the surface variables to an acceptable level but extrapolation beyond the historical conditions may be unreliable. The ability of the downscaling models to predict climate change impacts is greatly reduced due to the coarse spatial resolution of GCMs. This paper describes the application of a non-parametric approach called strategic re-sampling, in simulation of series along with the climate change scenarios. Using this method, these changes were conditioned upon observed series and then by using a weather generator, transferred to simulated series. The basic concept of the models is the non-parametric method of Nearest Neighborhood.Material and MethodsThe study was performed in 5 synoptic stations namely Tehran-Mehrabad, Mashhad, Tabriz, Qazvin and Bushehr stations. The target variables were maximum temperature, minimum temperature and rainfall. In this non-parametric simulation method, by defining a moving window with central feature vector and The atmospheric Aerosol Optical Depth (AOD) of the city of Tehran in each station was calculated using MODIS images. The calculated AODs were compared with pollution data collected at the station where a correlation coefficient of about 0.7 through a linear equation of (PM10 (μgr/m3) = 222.78*AOD - 65.091) was achieved. This is shown in figure (1) below: Fig. 1. A Model for extraction of PM10 from MODIS AODResults and Discussion Generally it seems that this amount of correlation for low spatial resolution data such as MODIS is acceptable. It should be noted that the AOD's pixel size are usually 10*10 km2 and therefore the ground underneath is covered only with a few of these pixels in each satellite passage, while the ground based measurement data are only valid in a limited distance around the station. This in many cases renders the accuracy of comparing satellite data and ground-based data difficult. One of the main sources of uncertainties was the MODIS AOD product low spatial resolution of 10 by 10 kilometers. While the results of measurement in the stations can hardly represents 100 square kilometers. ConclusionA positive relationship between aerosol optical depth and particulate matter of less than 10 nanometer size is discovered by many workers. To supply a model for this relationship use has been made from MODIS AOD products as well as in situ measurements of pollutions specially PM10. Despite low resolution of MODIS products, a correlation of about 0.7 was found between AOD and PM10 in Tehran. The results seem acceptable in situation of lack of continuous observation due to the present financial problems.

IntroductionThe Numerical Weather Prediction (NWP) models usually surface systematic errors in the forecasts of certain meteorological parameters. This drawback is a result not only of the shortcoming in the physical parameterization, but also of the inability of these models to handle successfully sub-grid scale phenomena. In order to reduce the influence of the above mentioned drawbacks in the final output of a NWP model, a variety of approaches based on statistical methods has been used. Most of them are derived from Model Output Statistics (MOS), which are able to account for local effects and seasonal changes. The limitation of this method and the similar ones is the necessity of access to long term data which are not always available. Among the methods that doesn’t need to long term data, Kalman filter is one of the most successful methods to this problem (post-processing) [Azadi et al, 2007; Chochet, 2004; Galanis and Anadranistakis, 2002; Homleid, 1995; Kalman and Bucy, 1961]. In this method, observations are recursively combined with recent forecast using weights that minimize the corresponding biases. The structure of Kalman filter algorithms is more suitable to describe linear procedures. For this reason, their application on meteorological parameters following a non-linear discontinuous behavior is always dubious. The method presented in this study, called, “non-linear Kalman filter” is based on the paper of Galanis et al (2006). Using this method, they investigated the use of non-linear functions in classical Kalman filter algorithms on the improvement of regional weather forecasts. After, application of the non-linear Kalman filter on the WRF model outputs, has been described for obtained an optimum polynomial for improving determination of neighbors’ matrix, neighbors are determined using Mahalanobis distance. Then by a stochastic process, the successive vector is selected for continuation of simulation. In this step, a perturbation subroutine is applied so that, the simulated series posses new values un-experimented variables. The strategic re-sampling technique has a stochastic function which is based on the observed data and it aims to generate new series with attributes defined by the user. This goal is achieved by defining a shape parameter, which enables simulating colder or warmer events or, wet and dry spells. Results and DiscussionBy implementation of the approach on observed data, the shape parameter values zero plus were obtained. As shape parameter becomes one plus, the departure from climatic normals would increase. It was observed that in values of 1 (for temperature series) and 0.85 (for rainfall series) the best results are hit. The successful performance of aforementioned approach is due to acceptable performance of weather generator, which produces the primary simulations. The T-test showed no significant deviation between the monthly mean values especially for mean temperature. Also, the F-test for comparing monthly variances, show some significant differences. Similar results were observed using the 1:1 line. The model performance in generating probability of events was evaluated using Linear Error in Probability Score (LEPS) test, in which the overall skill of model is determined from point skill of events. Good results were obtained based on statistic of the test. Conclusion The new approach of K-Nearest Neighbor was applied for simulation of temperature and rainfall variables in six stations of Iran. The improved model has been shown to be effective in producing precipitation amounts. Perturbation of the observed data points was performed by adding a random component to the values suggested by the basic K-NN model. This approach is philosophically similar to traditional autoregressive models except that the new values are obtained by adding a random component to the individual re-sampled data points. The main advantage of the model introduced here is that unprecedented precipitation amounts can be simulated for any given climate change scenario. Consequently, extremes precipitation, ones can be easily simulated. At present, the model considers changes in both temperature and precipitation simultaneously.