غلامعلی کمالی

Identifying the local people's attitude towards climate change and the traditional and modern solutions they took to adapt can be very useful in managing the effects of climate change. The purpose of this research is to present the views and attitudes of the native rice farmers of Mazandaran with climate change and adaptation strategies to it. In order to do this, first, the opinions of the local people about climate change and the strategies to adapt to it were measured using a questionnaire method, and they were processed using statistical methods. After determining the adaptation strategies index (ASI), 19 presented strategies were ranked so that the results can be used for more effective policies to adapt to climate change and provide better services to the local rice farmers of Mazandaran. The results showed that more than 50% of the rice farmers in the region have felt and experienced climate change and its effects, and the most important consequences of climate change in the rice sector are the reduction of soil fertility, the use of more fertilizer and the increase of pests and diseases, early planting and This is followed by the damage of the late spring cold, the increase in the time the seedlings stay in the treasury and the decrease in drinking and agricultural water, and solutions such as public fight against pests, provision of subsidized fertilizers and poisons, management of irrigation time and sustainable water management are the solutions selected by local people .

It has been tried to investigate and study the fluctuations of different meteorological parameters at different probability levels in the important stages of the agricultural calendar. To conduct this study, first, the traditional agricultural calendar was prepared through the reports of the Amol Rice Research Center and with the help of interviews with rice farmers and the cooperation of rice agricultural experts. Among the various stages in the agricultural calendar, 5 stages were selected as the main stages including: land preparation, treasury, transplanting, flowering and ripening of seeds and harvesting. Then, the data of temperature, precipitation, humidity, wind speed, sunshine hours and surface temperature in 9 weather stations as representative of the main rice growing areas in the province for the statistical period of 2005 to 2020 were received from the Meteorological Organization. . According to the agricultural calendar and the time frames for the 5 mentioned stages and the appropriate thresholds and weather conditions at that time, the compatibility of the favorable conditions for rice cultivation operations in different stages with the actual conditions of investigation and the expected values ​​of each parameter in each period It was calculated at different probability levels. The results show the amount and possible thresholds of the effective parameters in the 5 stages of the rice cultivation calendar. The results showed that the average temperature in the five mentioned stages is 12.8, 14.3, 18.7, 20.7 and 31.4 degrees Celsius, respectively, which is within the optimal thermal threshold range for all agricultural operations.

The amount of precipitation plays an essential role in the occurrence of floods. The more accurate the rainfall forecast is, the better the flood can be predicted. Numerical weather forecasting models such as WRF usually do not have suitable outputs for predicting the amount of precipitation in the first hours of implementation; This is intensified in the summer season on the southern shores of the Caspian Sea in the provinces of Gilan and Mazandaran. Because most of the heavy rains in this season occur in the form of convection and in small spatial and temporal dimensions. Using the extrapolation of GPM, TRMM satellite products to predict short-term rainfall up to 24 hours can be a suitable method to achieve the amount of rainfall with more appropriate resolution and accuracy. In this research, the occurrence of two daily precipitation systems of more than 40 mm in summer on the southern shores of the Caspian Sea was investigated. The results showed that the use of GPM, TRMM satellite products for short-term rainfall forecast up to 24 hours is more accurate compared to the model output. Statistical analysis showed a good correlation between model prediction and ground data and satellite data

The impact of climate change as the most important factor affecting agriculture, especially rainfed cultivation has challenged the management of these resources. This study tries to investigate the effect of climate change on the yield and length of the dryland wheat phenological stages in western Iran. For this purpose, two downscaling models, SDSM and LarsWG, were used to simulate the climate over the next 30 years. To model the performance and phonological stages, two models of AquaCrop and DSSAT in the base period and the future period were used, considering the three RCP climate scenarios of 2.6, 4.5, and 8.5. The results showed that the AquaCrop model has better performance and less error than DSSAT. So that the value of the coefficient of determination between observed and simulated data in the base period with AquaCrop model in Kermanshah, Sanandaj, and Ilam stations are 0.86, 0.64, and 0.89, respectively; and RMSE coefficient values are 198.6, 274.6 and 192 kg/ha, respectively. While, in the DSSAT model, the coefficient of determination is 0.90, 0.11, and 0.82, respectively, and the RMSE coefficient is 219.9, 288.1, and 238 kg/ha, respectively. The general results show that in LarsWG downscale model with AquaCrop and DSSAT agronomic model, the lowest yields are allocated to Kermanshah, Sanandaj, and Ilam in 8.5, 4.5, and 8.5 scenarios, respectively, and the highest yields are obtained in 2.6, 2.6, and 4.5 scenarios; which indicates a decrease in performance in the scenario of rising temperature and rising carbon dioxide. However, in the SDSM downscale model, the highest yield of dryland wheat is mainly in scenarios 4.5 and 8.5, and the lowest yield will be in scenario 2.6, which is different from the results of the LarsWG model. According to these results, it can be stated that the type of downscale model and crop model can be effective in the obtained results.

Rising global warming is one of the greatest challenges facing humanity in the 21st century. Therefore, it is very important to predict the maximum temperatures in order to know the amount of changes and, as a result, to take the necessary measures to adapt and moderate the adverse effects caused by it. Therefore, in this study, maximum temperatures were predicted in three provinces of Kurdistan, Kermanshah and Ilam in the west of the country. For this purpose, the data of two global models HadGEM2 and CanESM2 were used under three scenarios: RCP2.6, RCP4.5 and RCP8.5, as well as two microscale models of LARS-WG and SDSM, and changes in maximum temperatures on a monthly and annual basis in the future 2050-2021) compared to the base period (2018-1989) were examined in 17 meteorological stations. MAE, MSE, RMSE and R2 indices were used to calibrate and validate SDSM and LARS-WG models. The results showed that both models have a high ability to simulate the maximum temperatures of the study area. However, the SDSM model is more accurate than the LARS-WG model, with the lowest and highest accuracy for Bijar and Tazehabad stations with RMSE of 0.02 and 0.18, respectively. The results of maximum temperature forecasting also showed that according to both models, the maximum temperature in the future period will increase compared to the base period, which is the average of the studied models between 0.8 to 1.9 degrees Celsius in the region. Will be studied. The highest rate is estimated based on the RCP8.5 scenario. Spatially, the most changes are related to the northern and eastern areas of the study area and the least changes are related to the western areas of the study area.

It is important to prediction of precipitation changes to know how much it will change in the future and to consider the necessary measures to mitigate the adverse effects of climate change. Therefore, in this study, precipitation changes in the west of the Iran were predicted. For this purpose, the data of CanESM2 model were used under three scenarios of RCP2.6, RCP4.5 and RCP8.5 using the SDSM downscaling model and was investigated the changes of precipitation in the period (2021-2050) compared to the basic period (1989-2018). The MAE, MSE, RMSE and R2 indicators were also used to ensure the calibrasion and validation of the SDSM model. The results indicate the appropriate accuracy of this model in the study area. The results of a monthly survey of precipitation at the study station showed that precipitation will increase during the period (2021-2050) in most areas from October to December, and in some areas in addition to January to April. Based on this, it is expected that the amount of precipitation in the next period compared to the basic period will increase between 0.05 and 15% on average in the study area. Most of the changes are related to the northern and eastern areas of the study area and the least changes are related to the southern areas of the study area. Also, the highest and lowest changes are predicted based on RCP8.5 and RCP2.6 scenarios. Due to the complexity of the precipitation process, there are always uncertainties in the field of precipitation forecasting, and it is necessary to use the results of the models in this field with caution and to increase the accuracy of forecasting from different GCM models and microscale methods. Different used. Also, considering the increase and intensity of rainfall and mountainousness of the study area, the necessary strategies to deal with floods and its management should be considered.

Precipitation forecasting is an essential tool for optimal management of water resources and flood forecasting. The numerical weather prediction (NWP) models play a major role in weather forecasting. They predict the future state of the weather by mathematical modeling of the atmosphere behavior, based on its current condition. However, the accuracy of the NWP models is still a challenging issue and its improvement is the main goal of the operational prediction centers. In weather forecasting by using NWP models, there are several resources of uncertainty such as intrinsic chaotic behavior of atmosphere dynamic system, errors in observational data and initial conditions which are almost impossible to remove. Ensemble systems are used to quantify these uncertainties. Instead of only one deterministic forecast, an ensemble system is created by several forecasts obtained from perturbation of the initial conditions, physical schemes or dynamical core of the NWP models. Ensemble systems are widely used by the meteorological communities especially for medium-range weather forecasts, short range and even ultra-short range weather prediction. A probabilistic forecasting of flood and extreme precipitation can be produced by an Ensemble Prediction System (EPS). However in practice, ensemble forecasts are generally under-dispersive and thus are not calibrated, especially for meteorological parameters near the ground level. Several statistical methods have been proposed to post-process the EPS outputs. After post-processing the EPS outputs, the biases in both location and dispersion are removed using a historical database of ensemble forecast errors, and then a predictive probability density function (PDF) can be estimated. The most popular ensemble post-processing methods are Bayesian Model Averaging and Ensemble Model Output Statistics. In BMA method, based on the error statistics of each member during a training period, a PDF is first fitted to every ensemble member forecast. Then the predictive PDF is estimated by weighted averaging of members' PDFs. Logistic regression is a nonlinear regression method that is well suited to probability forecasting, i.e. situations where the predictand is a probability rather than a mea- surable physical quantity Logistic regression is a nonlinear regression method that is well suited to probability forecasting, i.e. situations where the predictand is a probability rather than a mea- surable physical quantity Logistic regression is a nonlinear regression method that is well suited to probability forecasting, i.e. situations where the predictand is a probability rather than a mea- surable physical quantity Logistic regression is a nonlinear regression method that is well suited to probability forecasting, i.e. situations where the predictand is a probability rather than a mea- surable physical quantity Logistic regression is a nonlinear regression method that is well suited to probability forecasting, i.e. situations where the predictand is a probability rather than a mea- surable physical quantity Logistic regression is a nonlinear regression method that is well suited to probability forecasting, i.e. situations where the predictand is a probability rather than a mea- surable physical quantity LR was among the first statistical methods that were used to post-process the EPS output. Logistic Regression (LR) was extended to provide a full continuous predictive PDF. In the extended Logistic regression (ELR), the predictions and thresholds are used as additional predictor variables. In this study, an EPS was developed using eight different configurations of the WRF model to produce probabilistic precipitation forecast over Iran. Initial and boundary conditions for WRF were provided from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) forecasts with a horizontal resolution of 0.5. The BMA and ELR methods were used to calibrate the probabilistic forecasts of rainfall in the fall and winter of 2016-2015 over Iran. The data were separated to two parts of equal periods. The first and second parts of the data were used for training and test respectively. The calibrated probabilistic forecasts were assessed using reliability and Relative Operating Characteristic Curve (ROC) diagrams, ROC Skill Score, Ranked Probability Score (RPS) and Ranked Probability Skill Score RPSS at the thresholds of 0.1, 2.5, 5, 10, 15 and 25 mm. For ensemble probabilistic forecasting, BMA was used as a statistical technique that combines inferences and predictions based on individual ensemble members, so as to yield a more skillful and reliable probabilistic prediction. It was assumed that the forecast PDF of a weather variable y is conditional on the ensemble member forecast f_k: p_k=(y|f_k). The BMA ensemble forecast is essentially an average of forecasts based on individual members weighted by the likelihood that an individual forecasting model is correct given the observations. LR was used as a nonlinear regression method that is well suited to probability forecasting, i.e. situations where the predicted is a probability rather than a measurable physical quantity. The mathematical form of the LR equation yields ‘S-shaped’ prediction functions that are strictly bounded on the unit interval (0<p< 1).

In this study, the performance of the Regional Climate Model version 4 (RegCM4.5) with different parameterization schemes was evaluated to simulating the seasonal 2-m temperature and precipitation over the north and west of Iran in the period of 1986-2015.. For this purpose, the NNRP2 reanalysis data were used as initial and boundary conditions of the RegCM4.5 climate model as well as six different parameterization schemes for simulation. The results demonstrated that the RegCM4.5 model has good potential for simulating precipitation and surface temperature in the north and west of Iran. The model bias for 2- temperature in different regions of Iran using parameterization schemes is different. Magnitude of the model bias for land surface temperature over different regions of Iran varies by convection parameterization schemes. In most cases, the root mean square error between post-processed simulated seasonal average temperature and observation value was less than 1°C and their correlation coefficient was more than 0.9. In general, according to the surface temperature simulations, using the model data after post-processing with Holtslag-Grell and Holtslag-Kuo schemas, compared to other simulations, achieved better performance on the study area. So, for the average seasonal precipitation, the Emanuel scheme is suggested along with the two Holtslag and UW PBL schemas with a correlation between 0.55-0.75 and a standard deviation close to 1. Also, this research intends to investigate the performance of model in simulating 2-m temperature and precipitation by developing a multi-physics ensemble for RegCM4.5 model over north and west of Iran. The investigation on RegCM4.5 model uncertainty in the ensemble prediction of temperature and precipitation by combining physical schemas, shows that the amplitude of the model uncertainty can be reduced and more reliable predictions of temperature and precipitation can be achieved. So that UW PBL-Grell and Holtslag PBL-Emanuel schemes had a good performance for ensemble prediction of temperature and Holtslag PBL-Emanuel had a good performance for ensemble prediction of precipitation.

Considering the significant effect of chilling and frost phenomenon on agricultural production in Iran, the aim of this study is projection of minimum temperature in three provinces of Kurdistan, Kermanshah and Ilam, Western of Iran. For this purpose, the data of 17 meteorological stations during the baseline period of (1989-2018) were collected. Then, the HadGEM2 and CanESM2 climate models outputs were statistically downscaled using LARS-WG and SDSM weather generators under three climate change scenarios of RCP2.6, RCP4.5 and RCP8.5 during future period (2021–2050) and compared with observed data. The performance of the two weather generators, were compared using MSE, RMSE, MAE and R2 indices. The results indicated the good accuracy of both statistical models in simulating the minimum temperature in the study area; however the SDSM model performed better than the LARS-WG. The projected changes of minimum temperature compared to the baseline period revealed a significant increase varying between 0.6 – 1.5 oC in study stations. The most significant change was observed in northern parts of study region especially in the Saqhez and Zarineh stations. The findings of this study can be used in frost risk management and agroclimatic planning in the region.

In this study, the forecast data are the accumulated 24 hours precipitation forecasts generated by the WRF model. Initialization time is 12 UTC during the period from 1st September of 2011 through 26th February of 2012. The initial conditions for forecasts are provided by the Global Forecast System (GFS) forecasts with 1- degree horizontal resolution. WRF is run with two nested domains. The large domain has a 45 km and the small domain has a 15 The small domain has a horizontal resolution of 15 horizontal resolution. Observational data used in this study consist of the cumulative precipitation observations measured at 0600 UTC in 306 irregularly spaced synoptic meteorological stations spread across Iran. To assess the performance of the ENSWM method, the data are divided into two sets of training and test period. The training period starts from September 1st through November 30th of 2011, and the test period include all the days between December 1st of 2011 and February 26th of 2012. The ensemble forecasts are generated using different configurations of the WRF model. Nine different configurations for the WRF model are used to build members of the system of consciousness.

The most important factors controlling the distribution of pollutants emitted from different sources and in different season are atmospheric circulations.This study dedicated to the investigation of the impact of two North Atlantic and Maden-Julian Oscillations (NAO and MJO) on Tehran air pollution during the period of 1984-2013. To show those oscillations relationship with the latter said event their correlations with the event were studied. As part of the correlation study the impact of time lag in the relation between those oscillations and the city air pollution was studied, too. Then meteorological fields and their anomalies that were considered effective on the formation of acute periods of air pollution, as well as the significant features and characteristics of atmospheric circulation at different levels were determined by using bootstrap and wavelet statistical methods. Afterwards, correlations between those identified anomalies and characteristics with different phases of the NAO and MJO oscillation was investigated. Accordingly, it was found that the accumulation of pollutants from local sources into the city atmosphere was mainly affected by the positive phase of NAO, which caused the warm and humid westerly air flows departed from the middle east and positive MJO phase especially the fourth phase and departure of convective cell to the eastern part of Indian ocean, during the cold and hot seasons. It was also found that the city atmosphere contamination with pollutants from regional sources mainly take place in both positive and negative phases of NAO. However it was more pronounced with shifts from positive to negative phase of NAO. The negative phases of MJO were also found effective in the formation of such circumstances especially the first phase and when the convective cell was at its closest distance from the deserts of the western and south western neighboring countries. Collectively, it may be stated that warm season contamination of Tehran’s atmosphere from local or regional sources is associated with weak MJO and negative phases of the oscillation (Phases 1, 2, 7 and 8), while contamination occurring during cold seasons is associated with MJO positive phases (phases 3, 4, 5 and 6).

Cyclonicity index shows the effect of the cyclonic or anticyclonic systems in a region based on the position of synoptic systems, the cyclonicity index was defined and its frequency was investigated.Five different types of atmospheric circulation, including trough edge, trough line, unknown pattern, ridge edge and ridge line, were classified according to the geopotential elevation curvature. As a result, the daily cyclonicity index time series was created; The frequency averages of atmospheric circulation types at the 500hPa level were, respectively, 81, 20, 106, 106 and 52 days per year. The cyclonic systems (trough edge and trough line) in the 27% of days of the year and anti-cyclonic systems (ridge edge and ridge line) 43% of days of the year were presented over the region. The spatial coverage of cylonicity index is about 300 kilometers radius from the central point and covering the area around 280,000 square kilometers. Strong correlation was observed between cyclonicity index at two 500 and 700 hPa levels that may be interpreted as the presence of transient weather systems over the study region. Mann-Kendall test showed a significant reduction in the frequency of cyclonic systems and an increase in the frequency of anti-cyclonic systems over the study region. It may be interpreted as longer dry periods in the study region.

This paper describes the behavior of dust storm occurred in Tehran, on April 24, 2017 by assessing the mesoscale and micro scale patterns, as well as meteorological parameters such as temperature, humidity, pressure, dew point, wind field, and visibility in the study area leaded to the occurrence of dust. This dust storm typically occurs at high wind speed, temperature drop, rise in dew point temperature and pressure, resulting reduction of visibility. The dust storm is also analyzed and tracked by satellite imagery. The RGB satellite images of the EUMETSAT sensor on April 24, reveals the motion of dust to the western, central Iran and the signs of dust genesis in Tehran province as well as the movement of Cirrus clouds to north and northwest of Iran as a signature of cold front. Cirrus clouds have been advanced to the northwest and center of Iran at 12:00 UTC. The concentrations of PM10 and PM2.5 particles can be considered as a suitable measure of the concentration of natural mobilized dust particles. The increase of PM10 and PM2.5 values is in coincidence with visibility reduction as well.

In this study, an ensemble system is developed using the WRF model to produce probabilistic precipitation forecasts over Iran. The ensemble system consists of WRF model simulations with eight different physical configurations. Initial and boundary conditions for WRF are provided from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) forecasts with a horizontal resolution of 0.5. The Bayesian model averaging (BMA) method is used to calibrate the probabilistic forecasts of rainfall in the fall and winter of 2016-2015. The 24-, 48- and 72-hour raw ensemble outputs were calibrated using the BMA technique for 90 days as the test period with 87 training days of forecasts. The calibrated probabilistic forecasts are assessed using reliability diagram, ROC diagram, Brier score and RPS. In addition, the forecast economic value has been investigated. The results show that the application of BMA has improved the reliability of the raw ensemble, such that the reliability and ROC diagrams have been improved significantly. For 24-hour forecasts, the Brier score is reduced by 24, 30, 32, 36, 39 and 65 percent at the thresholds of 0.1, 2.5, 5, 10, 15 and 25 mm, respectively. Similar results were achieved in 48- and 72-hour forecasts. The RPS score for the 24-, 48- and 72-hour forecasts is reduced by 45, 40 and 38 percent, respectively.

In this study,rossby wave activity flux,in order to investigate budget,direction and intensity of the incoming rossby waves,into the Middle East and Iran and its relation to positive precipitation anomaly in April and October of 2018 were used.dry period(April and May 2008)was selected to compare with the wave flux in the wet period.precipitation data from the Weather station of the country's Meteorological Organization were used to calculate precipitation anomaly,NCAR / NCEP data to compute wave activity flux.Results showed that two factors convergence(divergence)of the wave activity and the sources and sinks of energy,manage the energy budget in the Mediterranean region.In wet period,wave activity is stronger and there is an area of intense wave divergence on the east of the Atlantic ocean.The waves from the Atlantic ocean,by passing the Mediterranean Sea covers Iran.In the dry period,in the west of the Atlantic,the eddy activity is more weaker than the other.So the convergence of wave stream function on the Atlantic Ocean and also the weakness of the rossby on this area,followed by the divergence of wave stream function on the east of the Mediterranean Sea and of course Iran,and also the lack of appropriate eddy activity in this region, has caused drought in this period.

Drought varies with regard to the time of occurrence, duration, intensity, and extent of the area affected from year to year. The objective of this study was therefore to gather and analyze standardized information on Role of Early Warning Systems by FAO56 and UNESCO models for cereals (wheat, barley, corn and rice), leguminous (bean, chickpea, lentil and alfalfa) and industrial crops (soybean, sunflower, canola, sugare beat, potato and cotton) in Iran environmental zones. To gather information on perceived risks and foreseen impacts of climatic factors on crops production, we designed a set of qualitative and quantitative data from agrometeorological and agriculture organizations in 44 stations in Iran (1961-2010). Annual average rainfall (mm.y-1) and ETo (mm.y-1) in stations with very dry climate are 76.56 and 3001.03, respectively, these rates for stations with dry climate are 195.41 mm.y-1 and 2249.44 mm.y-1, for stations with semi dry climate is 343.9 mm.y-1 and 1351.62 mm.y-1, for stations with semi humid climate is 583.8 mm.y-1 and 1153.4 mm.y-1 and for stations with humid climate is 1272.16 mm.y-1 and 949.91 mm.y-1. The maximum and minimum of Annual average rainfall happened in Rasht (1337.5 mm.y-1) and Zabol (57.7 mm.y-1) stations, and the maximum and minimum for Annual average ETo happened in Chabahar (3909.15 mm.y-1) and Anzali harbor (890.6 mm.y-1), respectively. Therefore, 13.63 percent of stations have suitable conditions for crop productions and 86.37 percent are in critical and unsustainable conditions.

Introduction Temperature is a factor feels by human body and body sense changes when the outside temperature changes for the reason of the heat transfer between body and the environment.
Wind chill is caused by the wind in lower temperature and the higher the wind speed and the lower the temperature, the wind chill and its effects on the creatures would increase. The WCT is important, since it’s an index which shows people how to dress up or to be optimum on energy consumption. When it feels cold due to the wind chill, it could contribute greatly to decision making in urban design and fair and optimum distribution of fuel. The main purpose of this paper is to study the effect of wind chill on reducing the temperature that body feels, also when this done, it could help decision makers in social, political issues as well as peoples clothing and etc based on the wind chill index.
Material and Methods This research has been done about the determination of the temperature which our body feels based on the wind chill index in Isfahan.Isfahan with an area of about 105937 square kilometers has been placed between 30 degrees and 43 minutes to 34 degrees and 27 minutes of north latitude of equator and 49 degrees and 36 minutes to 55 degrees and 31 minutes of east longitude of Greenwich meridian. In this article we can calculate the wind chill index (WCT) from equation 1.
WCT=13.12.6215T-11.37V0.16.3965TV0.16 (1)
V: wind velocity in terms of km (hr) and T: air temperature in terms of °c
Results and discussion From the studies we have done the table results indicated that the month of October is the beginning of the wind chill and the end of it would be in May. But since the frequency of occurrence of this phenomenon in these two months is much lower than other months, will not be discussed in this article. Therefore April, November, December, January and February and March were considered to study. The occurrence of wind chill at Knur Biabanak station was 228 times during 23 years (8395 days). The highest of the wind chill occurrence was 2257 times in February and its lowest was 39 times in May. The wind chill has occurred in Naein station 432 times in the month of February. And this number is the highest occurrence of wind chill in a month.
Conclusion In this article, we have studied the effect of wind speed on the minimum temperature reduction in Isfahan province. Wind chill reduces the emotional temperature on the human body. The results of this research showed that the wind chill factor has been felt 2196 times at Isfahan airport station and 228 times at Khur station as the highest and the lowest numbers recorded, respectively. In the month of February we had 2257 times of wind chill occurrence in the statistical period that has been the most frequency of wind chill occurrence and the lowest number was 39 times in May. The minimum temperature and wind chill in the month of January respectively was -1.6 and -5.6 degrees Celsius. On average, wind blowing will cause the minimum temperature to reduce 4 degrees in this month. The zoning results of the minimum and the wind chill temperatures in Isfahan province indicate that the vulnerable areas of the wind chill in Isfahan are located in the center and western half (overlooking the mountains) of the province . In these regions some stations due to the topographic conditions are more vulnerable to the wind chill. These stations include Isfahan airport, Daran, Ardestan and Naien that they are the most vulnerable areas of the province against the wind chill. Also the month of February is considered as the most critical month in terms of wind chill effects on human body and the most widespread zone of wind chill happens in this month.

Climate is a major environmental factor that controls all aspects of life. Among the climatic elements temperature is the important factor and because of this, study the effects of temperature on the growth of organisms, especially plants will be necessary. If temperature to be below the critical level of growth, plant growing stages will be stop and may hurt plant. The critical level temperature is different and dependent on the kind of plants. The majority of biological changes in plants is dependent on Growing Degree-days. In the present study, it has been achieved Growing Degree Days (GDD) Zoning with using temperature indicators. According to these factors, it has been determined capabilities and limitations of agricultural climate in different plant species adaption in country. Wheat is one of the strategic agricultural products of Iran. This variety of wheat has the best yield and quality in north-western provinces of country, including West and East Azerbaijan, Ardabil, Zanjan, Kordestan and Hamedan. Despite the fact that the mentioned region has the area of 17578000 acres or nearly 16.3% of the country area, the area under cultivation of winter wheat in these provinces consist about 40% of total wheat cultivating farms in the country or about 2411000 acres. This implies that the region is prone of cultivating wheat (and also other agricultural products) about 2.3 times more than other parts of country.
Another fact that verifies this is fraction of area under cultivation of wheat per total province area. Among 31 Iranian provinces, ranking of this statistic shows that all the provinces in selected area rank below 10, including first rank in country, Hamedan Province with 21.2% of its area under cultivation of wheat, Second rank Ardabil Province with 20.0%, rank 4 Kordestan province with 18.4%, rank 6 Zanjan provinces with 14.1% and finally, rank 9 and 10 East and West Azerbaijan provinces with 9.7% and 9.6% respectively.
Although the most important source of impact on yield of wheat, especially in dry farming, is adequate and timely precipitation, the climate change and global warming plays an undeniable role on long-term trends and attitudes toward choice of new places for building farms to answer the consistently increasing demand for this strategic product.
The trend in four phenological stages beside total Lenth of Crop Season (LCS) as a measure of intensity of the effect of climate is calculated in 27 stations in the region.
Materials and methods For this research, it has been used daily temperature at 27 Synoptic Stations during 1981 to 2010 periods. For calculating heat units, it was used the thresholds of 5 and 10 ° C as the lower threshold and 30 and 35 C °for the upper thresholds. For determining of hit units, at first, the dates of starting and end of 5 and 10 C° temperatures were extracted and then heat units during the growing season was calculated from mean daily temperature minus the base temperature and zoning heat units maps in GIS were prepared.
Result and discussion According to this research, the growing season length of 5 and 10 C° were divided into five categories in Iran. In the research, the areas of the country that Thermal Units based on 5 and 10 were homogeneous had taken in a group. Heat units in the base of 5 C° are divided into five groups, including 2000-2500, 2500-3000, 3000-3500, 3500-4000, 4000> (GDD) and for 10 C° are divided into four group including 500-1500, 1500-2500, 2500-3500, 3000>.(GDD).
Conclusion Growing Season Degree-Days for the basis of 5 and 10 C° are declining from South to North and East to West in the country. The Heat Units amounts during the growing season at 5 C° are increased from South to North and West to East. These amounts are decreased from South to North for the base temperature 10 C° and are increasing from West to East. According to the obtained results, it can be suggested that in areas where the growing season is reducing, farmers by reducing crop yield and also products that have not been well face. That is better farmers used early growing plants until they got heat energy to supply in the plant during the growing season.

Climate is so important for the life and development of the living organisms on Earth. Among all the effects of different atmospheric variables on human comfort, the effect of combined wind and temperature is of particular significance. In this study, using statistical data of minimum temperature and wind speed of the synoptic stations during the period 1374 to 1393 ,which were obtained from the Meteorological Organization, the chill wind was zoned in the country .Results that were obtained from data analysis of 120 to 314 synoptic stations (for different time intervals) showed that we can consider the months of Aban to Farvardin as the start and end of wind chill in Iran, respectively.For days of Aban to Farvardin, the average reduction in minimum temperature of 4 degrees Celsius in Bahman due to the wind blowing has been felt.Zoning maps of the country that were traced for the cold months (from Aban to Farvardin), showed that the difference between minimum air temperature and the temperature that can be felt on human body (wind chill) is meaningful as well(Level of significance is in the range of 0.51 to 0.74). The highest frequency of wind chill is related to the year of 1390 with 18088 cases from 330 stations that are under study and the lowest is related to the year of 1374 with 4321 cases. .It should be noted that from the base year (1374) onwards ,the frequency of the wind chill occurrence has increased and the numbers of cold days because of the wind existence in the cities of the country are increasing too. by investigating the frequency of wind chill occurrence in the months which are under the study, the month of Bahman with47219 cases from 350 stations and the month of Aban with 17728 cases from 305stations, had the highest and the lowest frequency of occurrence.

Dust, as an aerosol, signicantly impacts air quality in arid and semi arid regions. Dust events in Iran, as a result of the semiarid climate, and its location in the global dust belt neighboring the deserts of Arabian countries, occur frequently. In recent years, the intensity of dust phenomena has increased, most especially in west and southwest Iran. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosols Optical Thickness (AOT) product is applied in order to estimate dust intensity. Since Aerosol Optical Thickness at 550nm has a close relationship with the brightness temperature of MODIS bands 31 and 32, and Normalized Dierence Dust Index (NDDI), the calculated AOT is proposed through quantitative analysis of MODIS data for major dust events over west and southwest Iran during years 2000-2009. The proposed calculated AOT matches MODIS AOT very well, with a squared correlation coecient of 0.740. Moreover, based on MODIS measurements, sequential separation of the dust cloud from the bright underlying surface and water cloud is accomplished through the Brightness Temperature Dierence (BTD) of suspended particle matter in 11 and 12 micrometer wavelengths of MODIS, NDDI and rened cloud threshold utilization. By statistical analysis of MODIS measurements, thresholds are determined over west and south west regions of Iran. Validations with ground meteorological observations over the region revealed good agreement of the proposed method in separating dust from the bright surface and cloud, which obviate the deciency of remote-sensing data products of dust particles near the source as a result of bright surface radiance contributions. In addition, there is considerable correlation between MODIS AOT at 550 nm and Ahwaz station PM10, while there is a negative correlation between calculated AOT and horizontal visibility. The statistical analysis and case studies reveal the accuracy of this method in extracting dust from underlying aerosols, and the usefulness of this technique in dust enhancement optimization.

Understanding the reasons behind the occurrence of significant storms and their rainfall distribution is a fundamental issue to increase the accuracy of the Meteorological and Hydrological predictions. The rainfall pattern is helpful in managing flood in urban basins and designing control structures for flood transmission. The time distribution patterns of heavy rainfall events have not been investigated much in Iran. This paper investigates the temporal rainfall distribution based on an analytical numerical method in southwest of Iran. This study selected the storm that happened on 12-15 November 2006 with precipitation of over 50 mm. First, rainfall data were calculated using the result of WRF numerical weather prediction model. Then, a temporal distribution of a logistic function has been fitted on rainfall data and the logistic coefficients of the rainfall curve were computed for the mentioned great storm event.
Rainfall information in the entire basin is not available due to irregular distribution and limited number of measuring stations. Thus, to study in detail and quantify the amount of rainfall in the entire basin with a regular grid, the best method is a simulation of Numerical Weather Prediction (NWP) models. Therefore, rainfall was simulated with the Weather Research Forecasting (WRF) model in two regular nested domains with the horizontal resolution of 27 and 9 km, from 47.5E to 53E degrees longitude and from 30.5N to 34N degrees latitude. The input Final operational Global Analysis (FNL) data was provided by National Center Environmental Prediction (NCEP). After running the model, three-hour interval rainfall outputs at all grid points were collected for the storm. The time distribution of rainfall in each grid point was computed based on the rainfall outputs of WRF and a three-parameter logistic function. To select the best values of the coefficients a and b, we used minimum Sum of Square Error (SSE) in the logistic curve fitting for every grid point. Since the greatest change in the slope of the logistic function is in the 70% middle of the temporal distribution curve of rainfall, the best values of the coefficients a and b are determined without 15% beginning and 15% end of the logistic curve. Since the range of the coefficient b is very large, the transfer function (B) reduced its variance. Changing the coefficients a and B leads to appearing logistic curve in various forms, which are the main parameters of the rainfall distribution. If the coefficient a increase as well as the logistic curve slope, more precipitation occurs at a fixed time in the dimensionless temporal rainfall distribution curve. Moreover, the time delay of the onset of the precipitation increases by changing the coefficient B.
Finally, maps of the coefficients a and B were drawn by using the IDW method in SURFER software.
The results show that the value of the coefficient a of the logistic function varies from 0.2 to 0.4 for southeastern and central parts and to 0.7 for northern and southwestern parts. Thus, in the north and south-west of the studied area, rainfall intensity becomes greater than in southeastern and central parts. Furthermore, the increase in coefficient is seen in the latitude from the bottom to up and changes of coefficient are not related to the longitude from west to east. However, it is related to changes in the topography of the area. The values of the coefficient B of the logistic function vary from 4 in southeast and central parts to 14 in south and north parts of the region. Therefore, any increase in coefficient leads to increase the time delay of the onset of the precipitation. To reveal more differences in the temporal distribution of rainfall, the studied area was zoned out to nine regions (A1, A2, A3, B1, B2, B3, C1, C2, C3) and the temporal distribution of rainfall was plotted for each region. Results show the maximum of rainfall intensity occurs from 0.42 to 0.58 of the total rainfalls time period in types A1, A2, A3, B3 and C2. Maximum rainfall intensity of types B1, B2 and C3 falls from 0.25 to 0.42, 0.25 to 0.67 and 0.42 to 0.83 of the total rainfall time periods, conversely. Additionally, the most of the total rainfall falls before 1/3 of the total rainfall time period in type C1. Thus, the maximum rainfall intensity occurs in early start time of rainfall.

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).

Climate change is a phenomenon that has made the most concern in communities and decision making centers. This phenomenon has important effects on different aspects of human life. One of the most important crops that are affected by climate change is wheat. Accordingly, data and information of Northern Khorasan have been used to study the effects of climate change on rain-fed wheat yield. For this reason, climatic data of Bojnourd synoptic station from 1984 to 2010 and economic data of Northern Khorasan rain-fed wheat available in Jihad Keshavarzi administration were used to analyze previous situation and detecting climate change and determine a relationship between economic yield and climatic variables. To determine relationship between economy of rain-fed wheat and regional climate, maximum and minimum temperature, annual precipitation and also cost of wheat production, price of wheat and barley, and cropping area have been selected as independent variables and yield and income of wheat have been selected as dependent variables. In addition, for prediction of the amount of climatic elements in future, numerical climatic meso scale models HADCM and LARS-WG have been used. The results showed that climate change occurred in 30 past years and there is significant relation between logarithm of maximum and minimum temperature and annual precipitation, and wheat yield. Using resulted equation for yield and income of wheat and the results from numerical climatic model showing 0.5 degree Celsius increasing of minimum and maximum temperature and decreasing 25 mm of precipitation from 2010 to 2039 in the region, increasing in wheat yield (10 kg.ha-1) and income (250000 t. thousand Rial-1) have been predicted in this region.