نشریه پژوهش های اقلیم شناسی
پیاپی 12 (زمستان 1391)

Whereas contemporary ecology mental (thinking) space has been filled by global warmness subject, there are a little literature that has been demonstrated day degree mechanisms trend discussion. Global warmness and its effects on human societies are one of the apprehensive subject that this warmness is the result of the hothouse (greenhouse) effect (Zangou et al, 2008). Day degree is the existent difference between the average weather temperature and selective temperature threshold, snow melting, freezing weather, plant growth, blossoming, products harvest, consumption energy for metropolis warmness and coldness and transportation systems that all of them are related to the relative value of these upermeasurments. (Kedia Glouseen 1999, Rahmano et al 2010, Jiango et al 2009) selective temperature thresholds for calculating coldness day degree trend is according to the specific purposes. For an example selective day degree for human relaxation is 18 degree. In this research for investigating (researching) in monthly trend gradient and trend of day degree, the coldness day degree with 25 degree temperature threshold, the daily temperature average during 44 years statistical periods (1340-1383) has been extracted from Sfazar data base. Coldness day degree data with cells 15*15 kilometer were mediated for all of the Iran, that its yield was a matrix by dimension 7187*44 (time * cells). Finally by the help of Mann-Kendall non parameter test, test trend and gradient the total trend for coldness day degree were calculated for every cells. The used software included Matlab and Surfer:a) First based on statistical the differences between observations calculate, one by one. (Raining, temperature, or any other ecological parameter).b) b) And n=the total observation that xj and xk respectively are the series j and k amounts. The above output function make every series sign clear as followed.After determining the sign the Variance of every observation will be calculated by the followed formulation the amount of observation should be higher than 10(n>10).c) The followed stage is Z statistical calculation. Discussion and Researching (investigating) the coldness trend in Iran territory represent the needs positive trend of coldness in summer and spring season. According to the figure 1 most of regions that have the same trend in the first 6 month of the year their trends are positive. In Farvardin and Ordibehesh months the internal hollowes, forests and southern coast (shores) exept for Oman Sea shore strip by having 45 percentage of Iran extent have the coldness needs positive trend. In Khordad months some strips of southern Zagros slope, the west of Kermanshah, Moghan plain and the east of Oroumieh Lake will add to these regions. By beginning of summer season coldness needs positive trend will represent in half of the country that its maximum location in Shahrivar month will be by 62 percentage of Iran territory. Most of regions that in the spring and summer seasons month have this trend their trend gradient are positive and their rate is 0 to 2 degree of day degree in the year and it means that for this rate in the year their coldness need will be increased. In Ordibehesht the quantitative maximum of the positive trend gradient belongs to the Post-coast of Oman Sea, Lout block medium, and Khouzestan plain by having 24 percentage of region extent and have the positive trend by 2-4 day degree in the year. In Khordad month by adding plain desert and Persian Gulf’s west shores, 29 percentage of country have the positive trend gradient (2- 4 day degree). In summer season observe intensive warmness trend in the middle of Lout and Kahnouj block as positive trend gradient of coldness needs of these regions is 4-6 day degree in the year. According to the positive trend and increasing the needs of Coldness in internal hollowes and forests and southern shores and as in Shahrivar month more than 60percent of country extent was witness of increasing trend of coldness could point out to being the more warmness of other Iran regions and shortend of autumn season. This is itsef represent the increasing the trend of temperature in ratio to temperature threshold (25 degree).The coldness needs negative trend in summer season in half of the west shahrekord and Khoramabad the west half of Birjand, the north of Sanandaj and Zanjan and east half of the Tehran give glad tidings the coolness trend of these regions. The most rate of trend gradient in year months is positive and is as the rate 0 to 2 day degree in the year. The coldness needs positive trend in the half of the year in the southern strip of the country and internal hollowes confirm the country warm regions temperature is increasing. 2 statement and contemporary outlook of study The warm regions of country have the increasing trend that cause to increasing energy consumption for coldness and energy consumption decreasing for warmness. Coldness needs negative trend in some of the high regions of country the coolness of temperature show to us the coolness of temperature in the mountain strip of country.

Climatic classification is one of the most useful methods in arrangement of information for similar places. Until now various kinds of climatic classification systems have been proposed for Iran concerning different goals. Buildings are affected by climatic situations in many ways. Due to this suitable recognition of climatic characteristics related to building design is essential. In this article assessing benefits of each climatic classification system for building design and architecture, defections of these systems have been noticed. Ignoring differences in thermal comfort zone and wind forces in various places are some of these defections. One of the most useful classifications for building design purpose is the HDD-CDD system proposed by Khalili. This system is based on the thermal comfort condition in air-conditioned buildings, ignoring natural ventilation potential. Depending on wind forces, natural ventilation could have significant effect on thermal comfort and though reduction of airconditioning systems usage and energy consumption. According to these, a new method has been proposed for Iran climatic classification based on natural ventilation potential and different thermal comfort zone. A typical office room in Yazd has been modeled for two months, April and July as moderate and hot month. An energy simulation program called Energy Plus was utilized to simulate the building thermal behavior. The required weather data had been derived from the Typical Meteorological Year (TMY) weather data used in EnergyPlus (EPW) weather file format. The simulations have been setup in four ventilation scenarios for each month: day ventilation, night ventilation, day and night ventilation and without ventilation. The results show thermal behavior of the simulated building affected by climatic parameters. The building also is taking advantages of natural ventilation potential which depends on wind speed and direction. The results show day and night ventilation as the best choice of ventilation. The acquired results determine the ventilation potential to keep building indoor temperatures within thermal comfort zone. In this method, factors of weather data in conjunction with natural ventilation potential characterize the place. This method is based on the HDDCDD values according to the specific thermal comfort zone for each place. The values for CDD in April have been decreased from 7856.88 to 6.68, with no ventilation and day and night ventilation, respectively. In June the reduction was from 13285.97 to 348.81. This means that using natural ventilation could decrease energy consumption of the building effectively. Accordingly the CDD value for each city would be different using various kinds of natural ventilation. A new climatic classification would be achieved by defining cities with similar CDD value and ventilation scenario. These values represent the natural ventilation potential of cities which depends on local wind forces. According to various goals and distinction factors, a climatic classification system would be useful for special purpose. Only some of the climatic classification systems are created in conjunction with building design. A new method has been proposed for Iran climatic classification based on natural ventilation potential and different thermal comfort zone. A typical office room in Yazd has been modeled as the sample to evaluate the proposed method. Simulations have been setup for two months in four ventilation scenarios: day ventilation, night ventilation, day and night ventilation and without ventilation. The reduction in CDD values is various using each ventilation scenarios. On the other hand the values for each city would be different according to various climatic parameters. The resulted values for HDD-CDD and natural ventilation potential show the best choice for ventilation. These values are the basic parameters to create the new climatic classification system based on natural ventilation potential proposed in this article. Applying similar simulations for other cities, it is possible to classify them based on values for natural ventilation potential and its type. Cities with similar CDD value and ventilation scenario would be in the same climatic zone. This method will consider available resources for wind as well as other climatic factors for selected city and also thermal comfort zone suitable for naturally ventilated buildings. Such classification will provide useful guidelines for architects to design naturally ventilated buildings with minimum need for mechanical cooling devices.

Probabilistic forecasts represent forecasts with a value between zero and one. Using ensemble forecasts is a proper way of getting probabilistic forecasts. An ensemble forecast is a group of forecasts which differ from each other in terms of initial conditions and/or physics of the model. A good probabilistic forecast should have reliability, sharpness and resolution (e. g. Wilks, 2006). For assessing reliability and sharpness of the forecasts, scores such as Brier score (BS), reliability diagram and Ranked probability Score (RPS) are used. Relative Operating Characteristic (ROC) curve is used to assess the sharpness of the probabilistic forecasts. Statistical post-processing techniques are used to produce calibrated probabilistic forecast. In this research two methods of rank-histogram (Hamill & Colucci, 1998) and logistic regression (Hamill et al, 2004; Hamill et al, 2008; Wilks & Hamill, 2007) are used to calibrate the raw ensemble outputs. Domain of study and data used Domain of study covers an area between 23-41 N and 42-65 E. Observed precipitations form 257 synoptic meteorological stations for a six month period from 1st Nov 2008 to 30th Apr 2009 are used to verify the EPS output. The EPS in this research is an eight member ensemble and includes five and three different configurations of the WRF and MM5 models respectively. Democratic voting In the so-called democratic voting method (Wilks & Hamill, 2006.) the probability of occurring precipitation less than or equal to a quantile q is calculated as follows: The coefficients b, b1 and b2 are calculated by minimizing likelihood function. Rank-histogram calibration If the nens members and the single observation all have been drawn from the same distribution, then actual future atmospheric state behaves like a random draw from the distribution. This condition is called consistency of the ensemble (Anderson 1997). In other words, if the ensemble members are sorted, then the probability of occurrence of the observation within each bin is equal. Suppose there is a sorted ensemble precipitation forecast X for a given time and location with N members, a verification observation V, and a corresponding verification rank distribution R with N+1 ranks representing the climatological behavior of the verification compared to the ensemble.Reliability diagram: Reliability diagram is graphical representation of observed conditional frequencies versus forecast probability. Forecasts with higher reliability represent lesser deviation from the diagonal line. Parts of the curve lying below (above) the diagonal line represent over-forecasting (under forecasting) for corresponding forecast probabilities. Reliability diagram Reliability diagram is graphical representation of observed conditional frequencies versus forecast probability. Forecasts with higher reliability represent lesser deviation from the diagonal line. Parts of the curve lying below (above) the diagonal line represent over-forecasting (under forecasting) for corresponding forecast probabilities. Fig. 1 1nd 2 show Brier score and skill score for the uncalibrated ensemble and calibrated probabilistic forecasts using Logistic and rank-histogram calibration methods. As shown in the Figures the BS decreases to lower values for calibrated forecasts and the degree of improvement is higher for Logistic method when compared to rank-histogram method. Reliability diagrams for calibrated and uncalibrated ensemble forecasts are presented in Fig. 3 for 24-h forecasts and different thresholds. Comparison of the reliability curves show that for all thresholds, the reliability curves for post processed forecasts are nearer to the diagonal line (perfect reliability) and hence show higher reliability. In other words the using logistic and rank-histogram calibration methods the probabilistic forecasts match better to the relative frequency of the observed occurrence of precipitation. Comparison the reliability curves for Logistic and rank-histogram show that for light precipitation threshold, the Logistic method is more reliable compared to the rank-histogram method while for heavy precipitation threshold the rank-histogram calibration give higher reliability. Calculated RPSs for both calibrated and uncalibrated probabilistic forecasts are shown in Fig. 4. RPS is a negatively oriented score and lower values dente more reliable and sharper forecasts. RPS for calibrated forecasts is smaller when compared to that of the uncalibrated forecasts. Using Logistic and rank-histogram calibration methods has improved the RPS 18 and 16 percent respectively for 24-h forecasts compared to uncalibrated forecasts. In general the results showed that using both Logistic and rank-histogram calibration methods improved the forecast probabilities in terms of both reliability and resolution compared to the raw ensemble forecasts. Also, results showed that for light and moderate precipitation thresholds the Logistic method gives more reliable probabilistic forecasts when compared to the rank-histogram calibration method. While for heavy precipitation threshold the reverse is true.

The Intergovernmental Panel on Climate Change (IPCC) stated that there is high confidence that recent climate changes have had discernible impacts on physical and biological systems. Impacts of climate change are felt most strongly through changes in extreme climate events, which are responsible for a major part of climate-related economic losses (Jiang, et. al. 2012). The state-of-the art General Circulation Models (GCMs) can reproduce important processes in global and continental scale of atmosphere and predict future climate under different emission scenarios. Since spatial resolutions of GCMs are often coarse (hundreds of kilometer), there is a mismatch of scale between GCMs and the scale of interest for regional impacts. Therefore, a range of downscaling methods have been developed to bridge the gap between the coarse resolution of the climate model outputs and the need for surface weather variables at finer spatial resolution (Wang et. al. 2011). Downscaling methods can be divided into two classes: dynamical downscaling (DD) and statistical (empirical) downscaling (SD). In this study, SD Model was evaluated by downscaling precipitation in the Kashafroud Basin. The statistical downscaling model (SDSM) used in our study here is a hybrid of a stochastic weather generator and regression methods (Wilby et al. 2001). This method includes a built-in transform functions in order to obtain secondary data series of the predictand and/or the predictor that have stronger correlations than the original data series (Wilby et al. 2004). Study area The KashafRoud basin, located between 58° 2´ and 60° 8´ E and 35° 40´ and 40° 36 ´N, totally has an area of about 16500 km2. To the north east of the catchment is the HezarMasjed Mountain, to the south west is the Binaloud Mountain and in the center of the catchment is the Mashhad plain. The climate of KashafRoud river basin ranges from severe semiarid to arid climate. The multi-year average precipitation and air temperature of the basin is about 220 mm and 12/2 °C respectively (Sayari et. al., 2011). Data: The data used for evaluation were large-scale atmospheric data encompassing daily NCEP/NCAR reanalysis data during 1961-2001 and the daily mean climate model results for scenarios A2 and B2 of the HadCM3 model during 1961-2099. Areal average daily precipitation data of the KashafRoud basin (Mean of four weather stations daily precipitation data) during 1969-2001 was used for downscaling. Modeling of four extreme precipitation In which R means the local predictand, L (l1, l2,..., ln) represents n large scale atmospheric predictors, and F is the built quantitative statistical relationship. SDSM uses large-scale atmospheric variables to condition the rain occurrence as well as the rainfall amount in wet days. It can be expressed as follows (Wetterhall et al. 2009; Wilby et al. 2004): in which i is time (days), ωi is the conditional possibility of rain occurrence on day i, i uˆ is the normalized predictor, αj is the regression parameter and ωi−1 and αi−1 are the conditional probabilities of rain occurrence on day i−1 and lag-1 day regression parameters, respectively. These two parameters are optional, depending on the study region and predictand. We used a uniformly distributed random number ri (0≤ri≤1) to determine the rain occurrence and supposed that rain would happen if ωi≤ri. On a wet day, rainfall can be expressed by a z-score as:In which Zi is the z-score on day i, βj is the calculated regression parameter, and βi−1 and Zi−1 are the regression parameter and the z-score on day i−1, respectively. As mentioned above, they are also optional; ε is a random error term represented by the normal distribution.Downscaling precipitation Calibration and validation of SDSM First, all of the 26 atmospheric variables in the region were taken as potential predictors, then most sensitive predictors for the region were analyzed month by month. The analysis results were integrated; and finally, 3 predictors were selected for predictand. The results showed that the pattern of change and numerical value of precipitation can be reasonably simulated. Although some differences existed between values of observed and simulated indices but the pattern of change in most of months were good. In the next 30 years, total annual precipitation would decrease by about 3.3 % in A2 scenario and 3.6% in B2 scenario and summer might be the most distinct season among all the changes in extreme precipitation indices.

Due to their high speed, flash floods are placed among the most important devastating disasters to humanity in contrast to other disasters such as drought and famine. The event is usually results from heavy precipitation. If the flood forecasting is done by an appropriate method, dealing with can be managed more properly. There are many parameters which are effective on models of flood forecasting. An essential component for flood forecasting is quantitative precipitation forecasting (QPF). At present, QPF is provided using limited area numerical models. The output of these local models is also used for operational and research purposes in Iran. Concerning the importance of QPF for flood forecasting of, in this research, the QPF output of the WRF model is used as an input for the HEC-HMS hydrology model to forecast the flood of Tire basin in Lorestan province of Iran. The Tire basin, in the west and southwest of Iran, is one of the sub-basins of the large Dez basin. In this research, hourly and daily precipitation data of rain gauges and also hourly discharge data from stations were collected and studied,. After preparation and qualitative control of the mentioned data sets, some preparations were applied for calibration of the HEC-HMS model and some of its hydrological parameters such as lag time, curve number and coefficient of maximum discharge were reconsidered. By topographic evaluation and assessment of soil and plant coverage of the region, needed preliminary data for performing of HEC-HMS model were estimated by trial and error method. After calibration and obtaining the optimum parameters, model verification was done using ther results obtained from 3 events thatwere not used for calibration already. For verification of rainfall-runoff models, forecasted precipitation of the meteorological WRF model was used. Simulated precipitations were used in the HECHMS model as an input and then runoff was simulated. Finally, simulated runoff was verified by statistical gauges. Three statistical criteria are computed in order to evaluate the capability of the coupled model including: the bias, the Mean Absolute Error (MAE), and the absolute relative error. The minimum MAE for the studied events was 13 (m3/s) and the maximum was 76 (m3/s). The minimum and maximum of absolute relative error for peak discharge in the studied events were 1.22, 41.4 (m3/s), respectively. The Minimum and the maximum of absolute relative error for volume of discharge in the studied events were 15.48 and 39.7. Time lags between the observed peak discharge and simulated peak discharge is calculated as 3 to 6 hours. Examining the results, we conclude that the coupled model is working much better for spring events in comparison to winter events. According to this research it can be said that the combination of WRF and HECHMS models increases the lead time of runoff prediction in real time forecasting. In spite of low errors in the forecasting, it can be said that the complete simulation were partly desirable. These results related to the tested cases in the research and generalizing of these results depend on to the more and extended research in the different fields and events. According to the importance of these kinds of forecasts, we suggest to eliminate the errors of these forecasts performing more studies and investigations.

Stable Climatic conditions such as thermal inversions and high-pressure systems established in stable weather, particularly in cold periods of a year increase the density and air pollutants mass in the layers adjacent to the ground surface. Spatial and synoptic scale winds are effective on the pollution aggregation as well. Tehran is considered as one of the most polluted cities in the world and on average one day out of each 3 days, is polluted by one or several main pollutants during the year. Air stability in both autumn and winter seasons; provide favorable conditions for thermal inversion and thus leading to pollution aggregation in the ground surface layer and breathing layer of people. The objective of this study is to analyze climatic causes of air pollution and its intensity in Tehran through studying the effects of synoptic scale atmospheric systems based on climate modeling. At First, the synoptic systems affecting Tehran were identified. Surveying the synoptic charts we looked for establishment of anti-cyclonic events in the city more than other atmospheric systems that are effective on air pollution intensification. In addition to establishment of stability and still air, the anti-syclonic events are important factors for creation of thermal inversions. Furthermore, during the cold season, mainly in high-pressure conditions thickness of mixed layer reduces due to air contraction and coldness. To detect the inversions, thermodynamic diagrams (SKEW-T) produced from upper air data of Mehrabad Station were used. At the next stage, using the TAPM numerical air pollution model, spatial and temporal distribution of polluted episods were analyzed based on the model outputs for surface wind speed and direction, and vertical profile of atmospheric elements. The air quality is controlled by variations of temperature and wind elements and the air pollution sources. Both of the weather elements may be considered as the most effective climactic factor affecting the temporal and spatial distribution of Tehran air pollution, so that the horizontal and vertical variations create different phenomena such as horizontal and ascending flows. Out of 4 identified types of atmospheric systems that affect air pollution intensity, two have the maximum frequency and effect on spatial and temporal distribution of the polluters, namely northwest anticyclone and Siberian highpressure. Between the two elements, the northwest anticyclone is more effective on the spatial distribution and the Siberian high-pressure is more effective for the temporal continuity of air pollution episods. Indeed, in this study by comparing synoptic and modeling analysis methods, it was concluded that the results for the descriptive analysis of the systems are justifiable for numerical analysis of overall air pollution in the city and climatic synoptic analysis is an appropriate method. In order to examine air pollution intensity locally in a district or city, modeling facilities are important because status of elements affecting air pollution such as wind as an important and effective climatic factor on urban polluter, can be analyzed and investigated in detail.

Drought refers to the short-term or long-term period’s association with the lack of rainfall, increased temperature and reduced humid22ity. Perhaps one of the most widely recognized drought indexes is Palmer Drought Severity Index (PDSI) presented by Palmer in 1965 to quantify the intensity of drought. Since then, numerous researches have been conducted based on this index and some attempted to modify the index. The general aim of this study was to derive and calibrate the empirical coefficients named as duration factors in the Palmer drought severity index by the method introduced by Wells et al (2004) in a country scale. Study area The study area located between latitude of 25 to 40 degree in North and longitude of to 64 degrees in East. Average annual rainfall is estimated as 240 mm per year. Palmer moisture model PDSI is based on a lumped soil moisture model with specific supply and demand. Supply of moisture from precipitation is absorbed into the soil. technique (latitude and longitude and altitude digitization). Rainfall data was also spatially distributed using an IDW interpolation method for each month of each year for the period of 1975 to 2011 at a national scale. Palmer model was performed on a distributed raster scheme with 4 kilometer spatial resolution. In Figures 1 and 2, spatial distribution of duration factors q and p is shown in the dry and wet cases respectively. As you can see, the coefficients show different spatial variations in wet and dry conditions and the maps can be used to extract the Palmer Index in any region. For a more detailed study, relative frequency curves of duration factors p and q are derived for the both wet and dry periods. The results indicate that value of p in the dry period is more in comparison to its value in the wet period and for q the situation is reversed. This means that in drought period, the index at each step, rather than changes in precipitation and soil moisture is sensitive to the value of the index on the previous step, while in wet period, the situation is reversed. This means that the climate of the study area (all areas) typically has a tendency to dryness. Conclusion The regional empirical coefficients of the Palmer Drought Severity Index in the last 36 years, indicating the need for calibration of Palmer (SC-PDSI) in most parts of Iran. As general, the climate of the study area (all areas) typically has a tendency to dryness.