@article { author = {Rivandi, Amir and Mirrokni, Majid and Mohammadiha, Amir}, title = {Investigation of Formation and Propagation of Dust Storms Entering to the West and Southwest of Iran Using Lagrangian Particle Diffusion Model, HYSPLIT}, journal = {Journal of Climate Research}, volume = {1392}, number = {13}, pages = {1-16}, year = {2013}, publisher = {https://www.irimo.ir/}, issn = {2228-5040}, eissn = {2783-395X}, doi = {}, abstract = {Introduction More than half of our land area is desert or semi-desert areas constitute one-fifth of the total area of the country. The phenomenon of wind erosion and dust storms are with the onset of wilderness areas and expand into adjacent areas, causing many problems for health, transportation, economic, activities and …. . Wind erosion is a serious problem in many parts of the world, especially in arid and semi-arid regions prone to wind erosion in North Africa, Middle East, parts of south, central and East Asia, Siberia and scattered. Climate scientists are convinced that a strong high pressure process large volumes of the vast wilderness of the west and southwest Asia fine dusts and will be transferred to the layer above the troposphere. Afterwards a lot of other strong currents fine dusts will move to higher latitudes. Materials and Methods In this study the synoptical weather maps were received from I. R. of Iran Meteorological Organization, and data of the National Centers for Environmental Prediction/National center for Atmospheric Research NCEP/NCAR and synoptic for the region were plotted to obtain the particle trajectory using a numerical modal of particle diffusion Lagrangian model. HYSPLIT was used to obtain the region of the storm and its path to check the information of the satellite images and satellite center in Yazd METEOASAT8 cloud seeding was used. 10 Dust storms events in the south west were selected. The list is given in table 2 HYSPLIT model was run for all selected output produced by synoptic maps and satellite images were compared with the model in all cases. The high resolution model stream utput to predict the formation and distribution of storms because of the importance of learning to storm Persian on June 2009 storm was fully investigated and stated synoptic maps were analyzed with the model output. Abadan weather station in the south west of the country was selected for review. Results and discussion The dust is not an unknown phenomenon especially in the central and western parts of Iran. But the frequency of occurrence and concentration which sometimes causing loss of visibility below 50 meters in recent years is considered as new phenomenon. The upper levels of the Iranian plateau in summer are dominated by tropical pleural pressure. The low pressure at the surface temperate increases. Low pressure depends on the Arabian Peninsula in northern Iraq Syria and create the right conditions for the rise of large amounts of dust into the air 1- A critical region in northwestern Iraq as a new center is formed dust storm that previously have been treated. 2- Depending on Iraq and northern Saudi Arabia causing low pressure conditions to climb the huge amount of dust in the air is Conclusion HYSPLIT model can predict the storm path. Airlines and warnings can use this model to identify sources of storm formation in Iran and surrounding countries. This model also has the high ability to predict the direction of dust particles or dimensional model is applicable along with that particular satellite images.    }, keywords = {Climate change,Windy erosion,Southwest of Iran,Lagrangian model,HYSPLIT,NCEP/NCAR}, title_fa = {بررسی تشکیل و انتشار طوفان‌های گرد و خاک ورودی به غرب و جنوب‌غرب ایران با استفاده از مدل پخش لاگرانژی ذرات HYSPLIT}, abstract_fa = {امروزه معضل افزایش فرسایش بادی و طوفان‌های گردوخاک بعلت تغیرات اقلیمی و خشکسالی‌های متوالی به بحرانی منطقه‌ای جهانی تبدیل شده است. برای منشأیابی، پیش‌بینی شدت و گستردگی و سرعت انتقال این طوفان‌ها از روش‌های مختلفی همچون مدل‌های عددی، تصاویر ماهواره‌ای و تحلیل‌های همدیدی استفاده می‌شود. در این مطالعه با بررسی و تحلیل نقشه‌های فشاری سطوح مختلف جوی، به اثر پارامترهای دما، فشار و سرعت باد در سطوح مختلف جو مؤثر بر انتشار طوفان‌های گردوخاک پرداخته شده است. در این مطالعه در مرحله اول به بررسی عوامل همدیدی موثر بر رخداد طوفان‌های گردوغباری ورودی از غرب و جنوب غرب به ایران پرداخته شده، در مرحله دوم برای مشخص کردن منشأ شکل گیری این طوفان‌ها از مدل پخش لاگرانژی HYSPLIT با استفاده از روش ردیابی پس‌رو استفاده شد. برای تایید خروجی این مدل عددی، از بررسی همدیدی استفاده گردید. خروجی‌های مدل نشان می‌دهد که به طور کلی منابع اصلی غبار برای طوفان‌های گردوغباری جنوب غرب ایران محدوده‎‌ای در حد فاصل مرکز تا شمال عراق، شرق سوریه تا شمال عربستان می‌باشد. برای مطالعه، یک دوره آماری 5 ساله از سال 1385 تا 1389 و 10 موج گردوغباری مهم در این دوره انتخاب و مورد مطالعه قرار گرفت. در این مطالعه برای ترسیم نقشه‌های همدیدی از داده‌های باز تحلیل NCEP/NCARبا دقت فضایی 5/2×5/2 درجه در راستاهای طول و عرض جغرافیایی استفاده شد.بنابراهمیتموجفراگیروگستردهگردوغباریروزهایسیزدهمتاشانزدهمتیرماه1388،الگوهایهمدیدومکانیسمتشکیل،انتقالوانتشار گردوغباردرآنبهتفصیلمطالعهشد درتمامموارد،استقراریکسامانهکمفشاربرمنطقهخاورمیانهوتقویتشرایطناپایداریدرسطح بیابان‌هاوهمچنینتاثیرهماهنگیکموجکمفشاردینامیکیبرفرازجومنطقه،زمینهمناسبرابرایانتقالریزگردهابهجومنطقهفراهم میآورد. در انتها نتایج با تصاویر ماهواره METEOSAT8 موجود در مرکز باروری ابرهای یزد مقایسه گردید که همخوانی بسیار زیادی مشاهده گردید.    }, keywords_fa = {تغییرات اقلیمی,فرسایش بادی,مدل پخش لاگرانژی,HYSPLIT,NCEP/NCAR}, url = {https://clima.irimo.ir/article_14128.html}, eprint = {https://clima.irimo.ir/article_14128_9e42a4d6663523b7b18bf4509929d7e9.pdf} } @article { author = {Bayat, Omid and Khademi, H and Karimzadeh, H. R.}, title = {Isotopic Thermometry and Past Climatic Reconstruction Using Paleo Pedologic Evidence in the Eastern Part of Zayandehrurd Watershed, Isfahan}, journal = {Journal of Climate Research}, volume = {1392}, number = {13}, pages = {17-30}, year = {2013}, publisher = {https://www.irimo.ir/}, issn = {2228-5040}, eissn = {2783-395X}, doi = {}, abstract = {Introduction Global warming and increase in greenhouse gases concentration have been accelerated during recent years and have become a global challenge (Woodward 1992). Changes in atmospheric circulation patterns and climatic parameters considered as a consequence of greenhouse gases acceleration. Climatic change occurs on diverse scales of time and space (Hardy 2003). Today, based on past evidence, present information and the modern technology, models are developed to investigate the climate change phenomena and its consequences (Woodward, 1992). Geological and biological evidence used for reconstruction of paleo climate parameters and validate of forecasting models (Moatamed 1997, Bradley 1999, Retallack 2001). Reconstructing ancient climates require consideration of many variables including temperature, the composition of the atmosphere and precipitation intensity (Dworkin et al 2005). Soils develop in direct contact with climatic parameters and can present indicators of past climate such as temperature, precipitation, seasonality, the composition of the atmospheric gases and atmospheric circulation patterns (Amundson et al 1996, Retallack 2001, Dworkin et al 2005, Sheldon and Tobar 2009). Despite data derived from soils are usually qualitative (Catt 1991), several attempts carried out for producing quantitative data during recent years (Dworkin et al 2005, Sheldon and Tabor 2009). There are different opinions about paleoclimate of Iran according to geomorphological investigations (eg. Krinsley 1970, Ramesht 1996, Moaieri et al 2009). The aim of this study is to use paleopedologic indicators for qualitative and quantitative paleoclimate reconstruction in arid lands of eastern Isfahan. Materials and methods The study area is located in the eastern part of Zayandehrud watershed (Fig. 1). Soil leaching index (SLI) and seasonality index for study area were calculated using modern climatic data according to Bull 1991. Two soil profiles were studied on an alluvial fan and a relict landform using criteria of USDA 1979. Redness rate index for Argillic horizons was calculated by method of Torrent et al 1983. Pedogenic carbonates were calculated from pedons on landforms. Oxygen isotopic analysis was used for this carbonates. Carbonates were reacted with 100% phosphoric acid to release CO2 gas. The CO2 gas was purified and analyzed using mass spectrometry at university of California, Berkley. Isotopic values reported in the standard δ notation relative to PeeDeeBelemite (PDB). Paleotemperatures was calculated using equations suggested by Dworkin et al 2005 and Maple 9.01 software. Results and discussion Modern climatic data of eastern Isfahan synoptic station show that mean annual precipitation is lower than mean annual evapotranspiration during the year and SLI is zero for the study area. This means there is no favorable condition for pedogenic development under present climate. Seasonality indexes indicate a seasonal pattern for precipitation and temperature in eastern Isfahan. Pedogenicdevelopment of studied profiles indicates formation of calcic and argillic horizons. Calcic horizons develop under semi-arid condition with seasonal aridity while argillic horizons develop under more moist condition (Retallack 2001). Mean annual precipitation about 400-600 mm (according to precipitation season) with seasonal pattern suggested for carbonates translocation and calcic horizon development (Tandon and Kumar 1999). It seems that calcic horizons in eastern Isfahan were formed under past semi-arid condition with seasonal pattern of precipitation. Formation and development of argillic horizons occur in more moist conditions than calcic horizons (Retallack 2001). Gvirtzman and wieder (2001) suggest annual winter precipitation about 600-800 mm for remove of carbonates from calcareous parent material and argillic horizon formation in the eastern Mediterranean. Expansion of calcic and argillic horizons in the arid lands of eastern Isfahan indicate past different hydrological conditions. Redness rate index for Argillic horizons in the studied profiles suggest that soils on relict landform were developed in warmer environment than soils on alluvial fan landform. Oxygen isotopes in carbonates considered for paleotemperature determination (Faure 1986). There is a strong correlation between temperature and δ18O values in meteoric water for out tropical regions (Cerling and Quade 1993, Bradley 1999). Oxygen isotopic diffraction between water and carbonate ions is a temperature dependent process (Moatamed 1997, Sheldon and Tabor 2009) and δ18O values in secondary calcite depends on δ18O values of mother water and crystallization temperature (Sheldon and Tabor 2009). Dworkin et al (2005) suggest two equations (spatial simulation and regression) for paleotemperature determination using oxygen isotopic values in pedogenic carbonates. Application of these equations for the studied carbonates show (a) pedogenic carbonates of eastern Isfahan formed in a colder environment than today with a decrease in temperature about 6°C (for alluvial fan) to 3°C (for relict landform) (b) carbonates on relict landform formed in a warmer environment than carbonates in alluvial fan environment, This result is in corroboration with redness rate index. Finally according to geomorphologic indicators of paleoclimate in central Iran, it seems that regression equation present better estimation of paleotempoerature for eastern Isfahan. Conclusion Analysis of climatic data show there are no favorable condition for pedogenic development under modern climatic condition in the eastern Isfahan. Paleo pedological and geomorphological indicators were used for paleoclimate reconstruction in eastern Isfahan (central Iran). Results indicate that studied carbonates and argillic horizons were formed under a semi-arid to sub-humid and colder environment than today. Climatic asymmetry observed during glacial periods in central and north Iran. It seems that activity of Siberia high pressure and subtropical anticyclone during glacial periods had a great effect on climate of Iran and was the main control for climatic asymmetry between central and north Iran.  }, keywords = {quaternary climatic changes,soils,Stable isotopes,arid region,Central Iran}, title_fa = {دماسنجی ایزوتوپی و بازسازی تغییرات اقلیمی گذشته با استفاده از شواهد پالئوپدولوژیک در بخش شرقی حوضه زاینده رود، اصفهان}, abstract_fa = {چکیده      پدیده‌های گرمایش جهانی و تغییرات اقلیمی در سال‌های اخیر شدت گرفته و به یک چالش جهانی تبدیل شده است. شناخت تغییرات اقلیمی گذشته در پیش بینی نوسانات اقلیمی آینده دارای اهمیت است. هدف از این پژوهش کاربرد شواهد پالئوپدولوژیک جهت بازسازی کیفی و کمی پارامترهای اقلیمی گذشته در مناطق خشک شرق اصفهان است.منطقه مورد مطالعه در فاصله حدود 50 کیلومتری شرق اصفهان قرار گرفته و دارای اقلیم خشک و شدیداٌ فصلی از لحاظ دما و بارش ااست. مورفولوژی خاک‌های مورد مطالعه پیشنهاد میکند رژیم اقلیمی گذشته منطقه شرق اصفهان بصورت فصلی با بارش زمستانه و تابستان‌های گرم و خشک همراه با میانگین سالانه بارش حدود 3 تا 4 برابر مقادیر فعلی (برای افق‌های کلسیک) و تا 6 برابر مقادیر فعلی (برای افقهای آرجیلیک) بوده است. بازسازی دماهای گذشته با استفاده از ایزوتوپ های اکسیژن در کربنات های پدوژنیک هم نشانگر تشکیل این کربنات ها در محیطی سردتر (دوره های یخچالی) نسبت به شرایط فعلی است. بازسازی دماهای گذشته پیشنهاد می‌دهد کربنات ها در مخروطه افکنه ها در محیطی با میانگین دمای سالانه حدود 6 درجه و در لندفرم رلیکت (فلات) با میانگین دمای سالانه حدود 2تا 3 درجه کمتر از شرایط فعلی تشکیل شده‌اند. ناهمگونی اقلیمی طی دوره‌های یخچالی در مرکز و شمال ایران مشاهده می شود.بنظر میرسد فعالیت‌های سیستم‌های پرفشار سیبری و جنب حاره‌ای در این مورد موثر بوده است.     }, keywords_fa = {تغییرات اقلیمی کواترنر,خاک‌ها,ایزوتوپ‌های پایدار,مناطق خشک,ایران مرکزی}, url = {https://clima.irimo.ir/article_14129.html}, eprint = {https://clima.irimo.ir/article_14129_a2d4181143b34f8f12f31ab8a0379128.pdf} } @article { author = {Hashemi Devin, Mehri}, title = {Evaluate the MEI Effect on Winter Precipitation In Northern Khorasan}, journal = {Journal of Climate Research}, volume = {1392}, number = {13}, pages = {31-44}, year = {2013}, publisher = {https://www.irimo.ir/}, issn = {2228-5040}, eissn = {2783-395X}, doi = {}, abstract = {Introduction North Khorasan province is in north east of IRAN and has different climates. West side has cold semi-arid, east side has cold arid and north part has cold semi-wet. Agriculture has special situation so seasonal prediction is very important. If precipitation predictions show above normal or below normal, agriculture should take a right decision for type of farming, dry farming or water farming. They are many studies about climate prediction in IRAN; some of them are about effects of sea surface temperature of Atlantic, Pacific and Indian Ocean on IRAN precipitation and the others are about the effects of teleconnections on climate prediction. These studies concluded that there is a relationship between changes of SST and precipitation fluctuations. Present study use CCA and MLR model of CPT to predict winter precipitation and evaluate the effect of seasonal MEI on North Khorasan winter precipitation. Materials and Methods Precipitation Data At this study the monthly precipitation data (Jan, Feb, and March) of 17 synoptic and rain gauge stations of North Khorasan during 1986-2008 is used and then the mean of winter precipitation is calculated. MEI data The MEI data (time series 12 month) is used of NOAA data bank from 1986 to 2008 and then the seasonal MEI data is calculated in excel and SPSS. MEI is computed on the six main observed variables over the tropical Pacific. These six variables are: sea-level pressure (P), zonal (U) and meridional (V) components of the surface wind, sea surface temperature (S), surface air temperature (A), and total cloudiness fraction of the sky (C). Reconstruction and Normal test data The Ratio Method is used to construct and complete the monthly precipitation data and then normal test data by using JMP4 software is done. The results show that yearly, winter and autumn data are normal. CCA Model Various dynamical and statistical models are used to predict seasonal and climate prediction. The most popular method and model that is used for seasonal prediction is CCA. IRI[1] released the Climate Predictability Tool (CPT) that provides a Windows package for constructing a seasonal climate forecast model, performing model validation, and producing forecasts given updated data at 2002. Although the software is specifically tailored for these applications, it can be used in more general settings to perform canonical correlation analysis (CCA), principal components regression (PCR), or multiple linear regressions (MLR) on any data, and for any application. For this study we use the last version of CPT (11.10) CCA and MLR model. Two data sets are required by CPT. the first data set contains the "X variables" here are seasonal MEI data from 1986 to 2008 and these variables are sometimes called "predictors", "independent variables". The Y variables are sometimes called "predictands", "dependent variables" here are winter precipitation (1986-2008). At first we choose spring MEI data as predictor and winter precipitation as predictant and by using CCA model we study the effects of spring MEI on winter precipitation and then choose summer and autumn MEI data and again implement the model. Model assumes a linear relationship between the predictor, x, and the predictand, y:                                      y=β0+β1x                                                 (1) Where β0 and β1 are regression constant and regression coefficient or the “slope”. Correlation coefficient is a widely used measure of the strength of linear association between the predictor and the predictand.   Where sx and sy are the standard deviations of x and y, respectively. The numerator in Eq. (2) is related to the covariance by a factor of n, and will be positive if positive anomalies in both the predictor and the predictand tend to occur in corresponding cases, and will be negative if opposite anomalies tend to occur. At this study negative correlations refer to winter precipitation will decrease by increasing MEI. The model expresses The value of a predictand variable as a linear function of one or more predictor variables and an error term.            (3) Here  is regression constant,  is coefficient on the kth predictor, k is total number of predictors,  is predictand in year i and  is error term. Model Validation After implementing the model by choosing cross validation method, Forecast performance scores and graphics can be obtained for the cross-validated forecasts. The performance window for an individual series provides a variety of forecast performance scores divided into those based on continuous measures, and those based on measures in which the observations, and in some cases the forecasts as well, are divided into three categories. The continuous forecast measures calculated are: Pearson correlation, spearman correlation, Mean squared error, Root mean squared error (RMSE) … and the categorical forecast measures are: Hit score, ROC area. Jajarm and Mokhaberat stations have the minimum RMSE that means their forecasts accuracy is high. Hit rate versus false alarm rate plots are also provided (ROC curve), which indicate how well the models forecast winter precipitation. The perfect prediction system would have a hit rate of 1.0 and a false alarm rate of 0.0. Winter precipitation forecasts with summer and autumn MEI have better results. Results The first five EOF modes can explain 89% of total precipitation variance. The model forecast winter precipitation for every station from beginning of duration (1986) so every station has time series of observations and hindcast. Most stations have positive correlation between winter precipitation and spring MEI and negative correlation between winter precipitation and summer and autumn MEI. The maximum correlation belongs to Langar winter precipitation and autumn MEI in CCA Model and Tazehgale in MLR Model and the minimum correlation is for Bolgan. Conclusion With regard to effect of teleconnection on precipitation, the relationship between MEI and precipitation is evaluated. The MEI time series are predictors and winter precipitation are predictants at CCA and MLR model. The results of this study are very important for North Khorasan agriculture. Comparison of model forecasts and precipitation observations of 2009 show us that we haven’t skillful forecast of seasonal precipitation by only use MEI and other teleconnections effect on seasonal precipitation, too.   1. IRI, INTERNATIONAL RESEARCH INSTITUE FOR CLIMATE PREDICTION}, keywords = {CPT,MEI,EOF,North Khorasan,winter precipitation}, title_fa = {ارزیابی اثر نمایه چند متغیره انسو بر بارش زمستانه خراسان شمالی}, abstract_fa = {از آنجا که کشاورزی در خراسان شمالی از اهمیت و جایگاه ویژه ای برخوردار است. پیش بینی فصلی بارش می تواند تاثیر بسیار مهمی در تابستان و پاییز در پیش بینی بارش زمستانه استان خراسان شمالی با به کار بردن نرم افزارCPT(Climate predictability tool) می‌باشد. بدین منظور از مدل تحلیل هم بستگی متعارف CCA (Canonical correlation analysis) و رگرسیون خطی چند گانه در نرم افزار CPT استفاده شده است. سری های زمانی فصلی نمایه MEI به عنوان پیشگوکننده و بارش زمستانه دوره 1986-2008 هفده ایستگاه خراسان شمالی به عنوان پیشگو شونده در نظر گرفته شده است. در روش تحلیل هم بستگی متعارف به منظور کاهش تعداد متغیرهای پیشگوکننده از روش متعامد تجربی EOF (Empirical orthogonal function) استفاده شد و 5 مؤلفه اصلی که 89% از کل واریانس مجموعه داده ها را شرح می دهند، انتخاب گردید. نتایج به دست آمده از دو مدل مذکور نشان می دهند که بین بارش زمستان و نمایه MEI در فصل بهار همبستگی ضعیفی وجود دارد و نمایه پاییز MEI همبستگی قوی تری با بارش زمستان خراسان شمالی دارد و بیشترین همبستگی از ایستگاه تازه قلعه و کمترین همبستگی از ایستگاه منگلی به دست آمد. منفی بودن همبستگی نشان دهنده این است که با افزایش نمایه MEI بارش زمستان کاهش می یابد و برعکس. بارش ها در تمامی ایستگاه ها نسبت به سال 2008 که خشکسالی به وقوع پیوسته بود، افزایش داشتند که مشاهدات نیز این افزایش بارش را تایید می کنند. اختلاف بین داده های خروجی مدل ها و بارش مشاهده شده نشان دهنده این است که فقط با تعیین فاز MEI نمی توان بی هنجاری بارش زمستان را از نظر علامت و  شدت پیش بینی نمود.    }, keywords_fa = {MEI,CPT، متعامد تجربی، خراسان شمالی، بارش زمستانه}, url = {https://clima.irimo.ir/article_14144.html}, eprint = {https://clima.irimo.ir/article_14144_32859d29a42c2ef9459b5f99c416a059.pdf} } @article { author = {Asgari, Ahmad}, title = {Harmonic Analysis of Atmospheric Pressure(Case Study: Over Tehran and Babolsar)}, journal = {Journal of Climate Research}, volume = {1392}, number = {13}, pages = {45-56}, year = {2013}, publisher = {https://www.irimo.ir/}, issn = {2228-5040}, eissn = {2783-395X}, doi = {}, abstract = {Introduction Atmospheric pressure among meteorological elements, is the only element that it’s relatively little changes can cause major changes of weather. Atmospheric pressure due to being influenced by diurnal temperature changes and by solar and lunar attractions has both diurnal and semidiurnal cycles (Ahrens, 2010). Fourier analysis method has been applied to those atmospheric elements including pressure that have periodic nature. Some researchers believe that more than one harmonic is necessary to analyze air pressure and other meteorological variables time series. Data and methods In this case study, this method was applied to atmospheric pressure time series of Tehran-Mehrabad as a station in the interior of Iranian plateau and of Babolsar as a station in coastal lowland region of Caspian Sea in 47-year period of 1961-2007. We also examined some other characteristics of atmospheric pressure for the two stations in the above mentioned period. According to Glickman (2000), harmonic analysis is “a statistical method for determining the amplitude and period of certain harmonic or wave components in a set of data with the aid of Fourier series. Harmonic analysis has been used in meteorology, for example, to determine periodicities in climatic data”. Results Annual average of QFF in Babolsar for period 1961-2007 is 1015.8 hPa. Low values of standard deviation (SD=0.72) and of coefficient of variation (CV=0.07) is indicative of low variability of annual averages of QFFs in Babolsar. Corresponding values for Tehran are 1017.3 hPa, 0.98, and 0.10 that confirm a bit more variability in this station. Figure 4 compares observed monthly averages of QFF in both stations with estimated values by first 3 and 4 harmonics. First three harmonics cover 99% of total variance in both stations. Conclusion Highest variability of monthly averages of QFF for the both stations was found in February. The only significant trend was found for annual averages of QFF over Tehran with r = - 0.55. First two harmonics show very well periodic variations of mean monthly sea level pressure (QFF) in both stations. Contribution of variance of first harmonic to total variance in Tehran (Babolsar) is 96.7% (93.9%) and of first two harmonics is 99.6% (99.4%). Best fitted frequency model for mean monthly QFF, among linear, exponential, power, polynomial, and logarithmic ones, was a fourth order polynomial with significant coefficient of correlation (r=0.475) for Tehran and a fifth order polynomial with significant coefficient of correlation (r=0.69) for Babolsar. Analysis of QFF at synoptic hours showed dominance of first harmonic (diurnal cycle) over second harmonic (semidiurnal cycle) in Tehran and vice versa in Babolsar. First and second harmonics covered 56% and 43% of total variance in Tehran and 47% and 52% in Babolsar respectively. Furthermore phases of first two harmonics were -22.5 and -1.2 in Tehran and -41.1 and -7.1 in Babolsar.    }, keywords = {Atmospheric Pressure,Harmonic analysis,Fourier analysis,Tehran,Babolsar}, title_fa = {تحلیل هارمونیکی فشار جو (مطالعه موردی: در تهران و بابلسر)}, abstract_fa = {فشار جو یکی از فراسنج های هواشناختی است که اندازه گیری صحیح، دقیق و تبدیل درست آن در یک تراز ثابت از اهمیت ویژه‌ای در هواشناسی و برخی از علوم برخوردار است. تحلیل هارمونیکی که یکی از روش‌های آشکارسازی تناوب‌ها در سری‌های زمانی منجمله سری‌های زمانی متغیرهای جوی است، در یک بررسی موردی برای فشار جو در ایستگاه‌های تهران مهرآباد واقع در منطقه داخلی فلات ایران و بابلسر واقع در منطقه پست ساحلی دریای خزر در دوره زمانی 2007-1961 انجام می گیرد. برخی دیگر از ویژگی های این عنصر نیز در دوره فوق‌الذکر مورد بررسی قرار می گیرد. در این بررسی دو هارمونیک اول به خوبی تغییرات میانگین ماهانه فشار تراز دریا (QFF) را برآورد می‌نمایند. در تهران (بابلسر) سهم واریانس حاصل از هارمونیک اول 7/96 درصد (9/93 درصد) و سهم دو هارمونیک اول 6/99 درصد (4/99 درصد) می‌باشد. در بررسی تغییرات طبیعی میانگین‌های سالانه فشار QFE و QFF نیز مدل‌های چند جمله‌ای در مقایسه با مدل‌های خطی، نمایی، توانی و لگاریتمی برازش بهتری را نشان می‌دهند. در دوره مورد مطالعاتی فوق الذکر، چند جمله‌ای درجه چهارم (475/0=r) برای QFE تهران بهترین برازش و چند جمله‌ای درجه پنجم (69/0=r) برای QFE بابلسر بهترین برازش را نشان می‌دهد. برای تحلیل QFFّ روزانه، از دیدبانی‌های سینوپتیکی (همدیدی) با فاصله زمانی 3 ساعته استفاده شد. تحلیل فوریه این فشارها نشان داد که در تهرآن‌هارمونیک‌اول (تغییرات شبانه‌روزی) غالب بر هارمونیک دوم (تغییرات نیمه شبانه روزی) و در بابلسر هارمونیک دوم غالب بر هارمونیک اول است. تناوب‌های شبانه‌روزی و نیمه شبانه‌روزی فشار در تهران به ترتیب 56 و 43 درصد و در بابلسر به ترتیب 47 و 52 درصد از کل واریانس را پوشش می‌دهند. زاویه فاز هارمونیک‌های اول و دوم در تهران 5/22- و 2/1- و در بابلسر 1/41- و 1/7- می‌باشد.        }, keywords_fa = {تحلیل هارمونیکی,تحلیل فوریه,فشار جو,تهران,بابلسر}, url = {https://clima.irimo.ir/article_14145.html}, eprint = {https://clima.irimo.ir/article_14145_61ad18db6f700d2c08bf2cd802d2405f.pdf} } @article { author = {Abbasi, Fatemeh and Ehteramian, korosh and Khazanedari, Leili and Gharaei, Sh and Asmari, Morteza}, title = {Locating the Most Suitable Dry Land Wheat Areas (Case Study: North Khorasan Province)}, journal = {Journal of Climate Research}, volume = {1392}, number = {13}, pages = {57-72}, year = {2013}, publisher = {https://www.irimo.ir/}, issn = {2228-5040}, eissn = {2783-395X}, doi = {}, abstract = {Introduction Since, wheat is one of the most important and strategic crops in Iran, determining appropriate areas for planting dry land wheat, according to the heat, humidity and climate capabilities in different regions of Iran, can increase the yield of this crop. In this research, the climatic potential and their adaptation with the requirements of dry land wheat (autumn wheat) are determined in North Khorasan province. Materials and Methods The region in question is North Khorasan Province that located in North East of Iran. In this study, long-term data of 17 rain gage, climatology and synoptic stations, 5 evaporation stations inside the province and 6 synoptic stations outside of the province during 1986-2005 were used in order to overlap better. The exact date of various growth stages of wheat in the different parts of this province is calculated by growing degree days (GDD) according to the following equation (Behnia, 1376):  Where GDD is growing degree days, Tmax and Tmin are maximum and minimum daily temperatures (Celsius degree) respectively, Tb is the base temperature (Celsius degree), a and b are the beginning and the end of date of phenological stage, respectively. The base temperature is the lowest temperature that is assumed to be no lower than the growth. The thermal units required for this crop are as follows: The total thermal units from sowing to sprout                                              180 GDD The total thermal units from sowing to flowering period                 1300 GDD The total thermal units from sowing to grain filling period              2100 GDD Discussion In this research, GIS is used for data analysis and processes. Then, by using growth requirements dry land wheat (optimum conditions), the data layers were classified and the weight of each zone was determined. Finally, by overlapping the maps in GIS, the dry land wheat climatic zoning map is prepared in North Khorasan. According to scientific references and climate conditions of study region, after determining the parameters for each of the stations and transferring them to GIS, four classes for each of the layers are defined (Table 1). In addition, for overlapping the layers according to climate conditions for planting dry land wheat and expert idea, the layers should have the same time scales, so the weight of each zone is assigned by a number from 0 to 100 (Table 2). By overlapping method (weight classes), all of the layers in table 2 are combined in GIS and finally land zoning map for dry land wheat is prepared in this province.     Table 1: Defined classes for each layer Qualitative Value' Climate Parameters Unsuitable weak Moderate Suitable Rainfall probability 3000 mm and more (%) < 30 30-50 50-70 > 70 Autumn rainfall amounts (mm) < 80 85-120 120-155 > 155 Spring rainfall amounts (mm) < 125 125-150 150-190 > 190 June rainfall amounts (mm) < 20 20-35 35-50 > 50 Suitable temperatures probability for germination (%) < 50 50-60 60-70 > 70 Temperatures probability 25 ºC and more during flowering stage > 50 37-50 25-37 < 25 Temperatures probability 30 ºC and more during grain filling stage > 50 37-50 25-37 < 25 Temperatures probability 9 ºC and lower during flowering stage > 35 25-35 15-20 < 15 Temperatures probability 9 ºC and lower during grain filling stage > 35 25-35 15-25 < 15   Table 2: Weight of each class   Annual Rainfall Autumn Rainfall Spring Rainfall June Rainfall Sprouting Stage Temperature Flowering Stage Temperature Grain Filling Stage Temperature Unsuitable 20 30 25 15 - 90 90 weak 50 55 55 50 65 70 70 Moderate 75 80 70 65 75 50 50 Suitable 95 100 90 85 90 25 25 Results Appropriate Lands: because of suitable climate conditions during the growing period of wheat, they have high yield. This area is 6263 Km2 (22% of the area province) and it is allocated in the north-east and north-west until the west of this province. Semi-Appropriate Lands: they are poorer than appropriate lands. Planting dry land wheat in this area is suggested and it can increase the yield of this crop. This area is 17534 Km2 (62% of the area province). 3. Weak Lands: these lands have low climate potential for planting dry land wheat. These regions are in the south-east and south-west of the province and they have lower annual rainfall and limited temperature in flowering and grain filling stages. This area is 4884 Km2 (16% of the area province) that are included Jajarm and Mangoli stations.    }, keywords = {Zoning,Dry land Wheat,GIS,Potential climatic,North Khorasan}, title_fa = {مکان‌یابی مناسب‌ترین مناطق کشت گندم دیم(مطالعه موردی: استان خراسان شمالی)}, abstract_fa = {با توجه به اهمیت عوامل اقلیمی در تولید محصولات کشاورزی دیم و نیز توانایی‌های بالقوه استان خراسان شمالی، پهنه بندی اقلیمی کشت گندم دیم در این استان هدف اصلی این تحقیق قرار گرفت. برای این منظور از آمار بلندمدت 17 ایستگاه باران سنجی،اقلیم‌شناسی، سینوپتیک، 5 ایستگاه تبخیرسنجی داخل استان و 6 ایستگاه سینوپتیک در خارج از این استان جهت هم پوشانی بهتر استفاده شد. در این تحقیق با توجه به تاریخ آغاز بارش‌های پاییزی برای هر منطقه از استان تاریخ کاشتی پیشنهاد شد. سپس مراحل مختلف رشد گندم دیم بر اساس محاسبه درجه- روزهای رشد (GDD) مراحل مختلف بدست آمد. در مرحله بعد، احتمال وقوع بارش سالانه 300 میلی متر و بیشتر، مقادیر بارش پاییزه، بهاره و خرداد با احتمال وقوع 75% بررسی شد. همچنین احتمال وقوع دمای مناسب جوانه زنی گندم دیم، دمای حداکثر 25 و 30 درجه سانتی گراد در مرحله گل دهی و مرحله پرشدن دانه محاسبه گردید. سپس با بهره گیری از نیازهای رویشی (شرایط اقلیمی مطلوب) گندم دیم، لایه های اطلاعاتی تهیه و کلاسه بندی شده و ارزش وزنی هر کدام از پهنه ها مشخص شد. در نهایت با تلفیق نقشه ها در محیط GIS نقشه پهنه بندی اقلیمی کشت گندم دیم در استان خراسان شمالی استخراج گردید. این نقشه نشان داد که مناسبترین مناطق کشت گندم دیم در قسمتهای شمال استان می‌باشد و مناطق ضعیف در جنوب غربی و جنوب شرقی استان واقعند. همچنین این تحقیق نشان داد که با انطباق لایه های موثر در فرآیند کشت گندم دیم در محیط GIS، امکان شناخت مناطق مستعد کشت برای این گیاه زراعی وجود دارد.  }, keywords_fa = {پهنه بندی,گندم دیم,GIS,پتانسیل اقلیمی,خراسان شمالی}, url = {https://clima.irimo.ir/article_14146.html}, eprint = {https://clima.irimo.ir/article_14146_b697a8b351446f3dd473ea0592f3c43f.pdf} } @article { author = {Halimi, Mansour and Takhtardeshir, Ashraf and Rostami, SH}, title = {Compression Spatial Interpolation Models in Estimation the Heating and Cooling Degree Days in Iran}, journal = {Journal of Climate Research}, volume = {1392}, number = {13}, pages = {73-84}, year = {2013}, publisher = {https://www.irimo.ir/}, issn = {2228-5040}, eissn = {2783-395X}, doi = {}, abstract = {Introduction Nowadays with regard to the patterns of the energy consumption, identify the factors and components that affect it, has been the most important approach of the geosciences issue. The cooling and heating degree days (CDD & HDD) are the Indices for potential of energy consumption in any region. Therefore identify Iran’s different region from scope of this climatic indices can be very useful in planning of energy consumption. Spatial interpolation methods are frequently used to estimate values of climatic data in locations where they are not measured. However, very little research has been investigated the relative performance of different interpolation methods in data of CDD & HDD in Iran. Actually, it has importantly practical significance to as far as possibly improve the accuracy of interpolation results for climatic data, especially in Iran that have inadequate number of meteorological station. Therefore the main objective of this paper is comparison of three spatial interpolation methods for estimating CDD and HDD as a climatic variable in not measured location. Material and method This paper focuses on determination the relative performance of 3 different spatial interpolation methods for estimating CDD and HDD of Iran and then select optimum method for estimation this variable as a climatic index of potential of energy consumption. HDD and CDD provides a simple metric for quantifying the amount of heating and cooling that buildings in a particular location need over a certain period (e.g. a particular month or year) it calculated over a period of time (typically a month) by adding up the differences between each day's mean daily temperature to base temperature (65 and 70˚F For HDD and CDD, respectively). The mean seasonal observed data of cooling and heating needs, are collected from 30 meteorological stations for the period 1965- 2005. Inverse distance weighting (IDW), ordinary kriging (OK) with different Semivariogram and tension Spiline, are selected as interpolated methods. Moreover, cross-validation (CV) is used to evaluate the performance of different spatial interpolation methods. 4 statistical criteria named: Mean Absolute Error (MAE), Mean Bias Error (MBE), Root Mean Square Error (RMSE), an acceptability index (d-Willmote) and finally coefficient of determine (R2), are used to analyze and compare the performance of these interpolation methods. Results and discussion The results indicated that all models have taken overestimate error to prediction of HDD and underestimate error to prediction CDD. Also we found that the average of observed data is 345.5 for HDD and 157 for CDD and the average of estimated data from IDW, Kriging and Spiline for HDD and CDD is 363, 363, 346 and 138,142, 148, respectively. And can dedicate the best model for estimating the general and total attitude such as average is tension Spiline. But for prediction of spatial detail and spatial construction that is most important in this paper, the Krigin model with the spherical Semivariogram has minimum MAE, MBE, RMSE (89, 17, 128) for HDD and (69, -15, 90) for CDD and is considered as the optimum model for interpolating this climatic variable. Conclusion This paper results indicated that the Kriging model with the spherical Semivariogram is more appropriate to estimate the CDD and HDD. The output of this paper can help decision maker to have more clear identification of the climatologically potential for the energy consumption in Iran. And could be useful for climate adapted architecting.    }, keywords = {CDD & HDD,Cross-validation,Interpolation,GIS}, title_fa = {ارزیابی و دقت سنجی روش‌های درون‌یابی مکانی در برآورد نیازهای گرمایشی و سرمایشی ایران}, abstract_fa = {      امروزه با توجه به اهمیت مباحث مربوط به الگوها و روش‌های بهینه سازی مصرف انرژی، شناسایی عوامل تاثیرگذار بر مصرف انرژی از مهمترین رویکردهای رشته‌های مرتبط با علوم محیطی می‌باشد. هدف این تحقیق، گزینش بهترین روش درون‌یابی برای پهنه‌بندی نیازهای گرمایشی و سرمایشی ایران به عنوان شاخص اقلیمی معرف پتانسیل مصرف انرژی هر منطقه است. در این راستا ابتدا با استفاده از دادههای درجه روزهای گرم و سرد30ایستگاه همدیدمراکز استان های کشور، در دوره آماری 1965-2005از طریق به کارگیری روشارزیابی متقابل موجود در ابزار زمین آمار سامانه اطلاعات جغرافیایی، اقدام به مقایسه 3 مدل درون یاب اسپیلاین کششی، کریجینگ معمولی با نیم تغییرنمای کروی و وزن دهی عکس فاصله با توان وزنی 2 و دخالت 8 همسایه نزدیک، گردید. از طریق تحلیل شاخص های دقت سنجی مقادیر میانگین خطای مطلق، میانگین خطای اریب و ریشه مربع میانگین خطاها و در نهایت شاخص قابلیت پذیرش ویلموت و ضریب تعیین، به مقایسه و دقت سنجی 3 روش درون یابی مذکور در تخمین نیازهای گرمایشی و سرمایشیایران پرداخته شد. نتایج تحقیق گویای آن بود اولا همه مدل ها در تخمین نیازهای گرمایشی دچار خطای بیش برآورد و در تخمین نیازهای سرمایشی دچار خطای کم برآورد شدند. مدل اسپیلاین برای برآورد روند های کلی متغیر مورد نظر مانند میانگین کل، نسبت به دو مدل دیگر به مقدار واقعی بسیار نزدیکتر بود. اما از لحاظ دقت برآورد فضایی و ساختار فضایی و جزییات تغییرات مکانی نیازهای سرمایشی و گرمایشی مدل زمین آماریکریجینگ معمولی با نیم تغییرنمای کروی به عنوان مدل بهینه درون یابی این متغیر اقلیمی انتخاب شد. خروجی کار می‌تواند در شناسایی مناطق مختلف کشور از لحاظ نیازهای گرمایشی و سرمایشی به عنوان شاخص اقلیمی معرف پتانسیل مصرف انرژی در راستای اصل 19 مقررات ملی ساختمان و مسکن بسیار مفید واقع شود.    }, keywords_fa = {نیازهای گرمایشی و سرمایشی,روش‌های درون‌یابی,ارزیابی متقابل,ایران}, url = {https://clima.irimo.ir/article_14150.html}, eprint = {https://clima.irimo.ir/article_14150_99f848f7a1347d18c1c0e0531a2dbbdd.pdf} } @article { author = {Akbarzadeh, Mehri and Mobasheri, Mohammd Reza and Fatemi, Seyed Bagher}, title = {Evaluation of MODIS 16-Days-Albedo Products Using ASTER Albedo Products for Homogeneous Semi-Arid Surfaces}, journal = {Journal of Climate Research}, volume = {1392}, number = {13}, pages = {85-96}, year = {2013}, publisher = {https://www.irimo.ir/}, issn = {2228-5040}, eissn = {2783-395X}, doi = {}, abstract = {Introduction Nowadays, evaluation and monitoring of change in environment and climate at the regional to global scale is an important issue for nowcasting and forecasting purposes. Surface Albedo is a key parameter in climate studies. Land surface Albedo is a product of Moderate Resolution Imaging Spector radiometer (MODIS) in global scale but in low resolution where the access to these products is free. However, Climate model outputs could be affected by errors in MODIS Albedo products. In this paper, the accuracy of Short wave Broadband Albedo estimated from MODIS global Albedo products (MCD43A3) is evaluated. The field used for this purpose is a plain homogeneous, semi-desert and non-vegetated region near the Qom-Tehran highway. Since MODIS and ASTER sensors are both on board of the same platform and due to the higher spatial and radiometric resolution of ASTER sensor, ASTER Albedo products are used as ground truth for evaluating MODIS Albedo Products. Materials and Methods The data used in this work consist of MODIS Albedo products, ASTER images, reflectance data, MODIS optical depth data, climatological data and land cover maps. To extract albedo from ASTER image, the geometric, atmospheric and radiometric corrections were implemented first. Then using the surface reflectance of ASTER, the shortwave broadband albedo was calculated. To calculate the MODIS actual albedo, the sun zenith angle image was co-registered with respect to ASTERs. Then after calculating fraction of diffused skylight, the MODIS actual albedo was calculated. Then to compare albedo images of ASTER and MODIS, the ASTER albedo products were resampled to 500m pixels. This was done by averaging all ASTER albedo values locating in a MODIS pixel. Results and Discussion In this work, using ASTER shortwave albedo, MODIS shortwave albedo was evaluated. Comparison between these two products showed that the ASTER albedo was more sensitive to the daily atmospheric conditions as well as surface cover than the MODIS albedo. However it is seen that the 16 days average of MODIS albedo reduces these effects. Also it is seen that except on Oct23, 2004, the MODIS shortwave albedos were always smaller than those of ASTERs. However, precipitation and increase of soil moisture reduces albedo values where this for ASTER albedos were more pronounced. Also it was seen that the RMSD between ASTER and MODIS shortwave albedo values on Sep14, 2003 and Oct23, 2004 were greater. The reason for this may come from the fact that on Oct23, 2004 we had raining and on Sep14, 2003 we had strong wind from nearby salt lake. On the other hand, on Sep29, 2004 and Jul28, 2001, the atmospheric condition for ASTER acquisition day and for 16 days MODIS passage was the same where the difference between these two products minimized. Based on this, if the weather condition for the ASTER acquisition date and 16 days period of MODIS were the same, a correction of 4% increase to MODIS albedo values is suggested otherwise based on the severity of the weather conditions, this difference could be enormous and the MODIS albedo values are less reliable. Conclusion In this work, the actual MODIS shortwave albedo products were compared to those of ASTER shortwave albedos. The result of this investigation in years 2001 to 2004 showed that whenever the weather conditions in the ASTER acquisition date and 16 days period of MODIS were the same, a correction of 4% increase to MODIS albedo values is needed. Otherwise if the weather conditions of ASTER acquisition day were different from MODIS 16 day’s period, the difference between these two albedo products may reach to 9%, this difference could be enormous and the MODIS albedo values are less reliable. Due to the importance of surface albedo values in meteorological and climatological models, the error in estimation of albedo may cause erroneous conclusion in the environmental studies.      }, keywords = {Albedo,MODIS,Aster,Remote Sensing}, title_fa = {ارزیابی محصولات آلبیدوی 16 روزه MODIS با استفاده از آلبیدوی ASTER در مناطق نیمه خشک با پوشش همگن}, abstract_fa = {      امروزه ارزیابی و کنترل تغییرات زیست محیطی و آب و هوایی در سطح منطقه ای و جهانی، از منظر پایش شرایط فعلی و امکان پیش بینی تغییرات آینده، از اهمیت ویژه‌ای برخوردار است. آلبیدوی سطح از پارامترهای مورد نیاز در مطالعات زیست محیطی و آب و هوایی می‌باشد. سنجنده‌ MODIS، آلبیدوی سطح زمین را بطور مستمر در مقیاسی جهانی ولی با قدرت تفکیک مکانی پایین تولید و بصورت رایگان در اختیار عموم قرار می‌دهد. خطا در محصولات آلبیدوی این سنجنده می‌تواند نتایج خروجی مدل‌های آب و هوایی و اقلیمی را تحت تاثیر قرار دهد. در تحقیق حاضر دقت آلبیدوی پهن‌باند موج کوتاه حاصل از محصولات آلبیدوی MODIS (MCD43A3) در منطقه‌ای همگن، نیمه خشک، فاقد پوشش گیاهی و تقریباَ هموار واقع در اطراف اتوبان قم-تهران مورد ارزیابی قرار گرفت. با توجه به قرار گرفتن سنجنده‌های MODIS و ASTER بر روی یک ماهواره و قدرت تفکیک مکانی و رادیومتریکی بالای سنجنده‌ ASTER از آلبیدوی موج کوتاه آن برای ارزیابی آلبیدوی واقعی موج کوتاه حاصل از محصولات آلبیدوی MODIS استفاده شد. نتایج بررسی‌ها در سا‌ل‌های 2001، 2003 و 2004 نشان داد چنانچه شرایط جوی در طول دوره زمانی تصویر آلبیدوی MODIS یکنواخت و مشابه با شرایط جوی در تاریخ تصویر ASTER باشد بیشترین اختلافات نسبی آلبیدوی MODIS نسبت به ASTER، حدود 6 درصد و RMSD نسبی این اختلافات تقریباً 4 درصد می‌باشد .در مواردی که شرایط جوی روزانه و متوسط 16 روزه تفاوت قابل توجهی داشته باشد میزان اختلاف بیشتر خواهد بود که در شرایط حاکم بر این تحقیق، این اختلاف و RMSD اختلافات به ترتیب در حدود 11 درصد و 9 درصد به دست آمد.    }, keywords_fa = {آلبیدو,MODIS,Aster,سنجش ازدور}, url = {https://clima.irimo.ir/article_14151.html}, eprint = {https://clima.irimo.ir/article_14151_9d42888d8ffcd0217521dc3bf0912923.pdf} } @article { author = {Khosravi, Mahmoud and Bostani, Mohsen and Azizoghli, Mohammad Ali and Goodarzifar, Mosadeg}, title = {Comparison of the Sistine and Baluchistan Province Precipitation Zones Using Satellite Data and Ground Stations}, journal = {Journal of Climate Research}, volume = {1392}, number = {13}, pages = {97-110}, year = {2013}, publisher = {https://www.irimo.ir/}, issn = {2228-5040}, eissn = {2783-395X}, doi = {}, abstract = {Introduction Rainfall is the most variable climatic elements. The changes of Precipitation in the location and the time dimension are large. Climate zoning and spatial resolution similar regions in terms of climate Regional development planning and investment capabilities across the country, including Sistan-Baluchistan province seems necessary. Materials and methods For zoning Sistan and Baluchestan province of precipitation two series of satellite (TRMM) data With Resolution 0.5 * 0.5 degrees and 145 rain gauge, Climatology and synoptic stations for a period of 12 years (2000-2011) are used. For this division on the two data series through principal component analysis and cluster analysis the ward method was applied. The monthly precipitation data from stations placed in a matrix of rows and columns of the matrix dimensions of 145 rain stations. In the next step the correlation between annual precipitations measured data and the calculation results in a matrix called the correlation matrix (R) inserted. At Continuation the results of Rotated component Matrix with varimax method As Input Cluster Analysis reused. The procedures were done for both data Series. Satellite (TRMM), Tropical Rainfall Measuring operations, a joint mission of the United States National Aeronautics and Space Agency (NASA) and the Japan Aerospace Exploration Agency (JAXA) is. The Satellite Monitor and study tropical rainfall And How Effect the Precipitation on Global climate. Results and discussion The study performed on Weight matrix and clustering of the stations data, Province divided into three broad zones of Extensive low precipitation, Makran average precipitation and Saravani high precipitation. Analysis of results the principal component analysis and clustering of precipitation with satellite data (TRMM) Province Divided into three zones of the northern high precipitation, central low precipitation and southern average precipitation. Comparison of zoning with two data series Indicates that Zoning with satellite data has a high error in Precipitation division. So that the North West and west of Province Which has a low height above sea level and away from sources of moisture are more Most precipitation in the region shown And the central area where Located the Makran Mountains And Indian summer monsoon system has benefited Defines the area with low precipitation in this province. Conclusion This study compares the Zoning Two Data Series Ground stations and data Sensor Satellite (TRMM) took place. According to Located in this province Close to tropical, Satellite data With Actual data At Zoning rain Province Case Was compared the following results were obtained. 1. Data of Sensor Satellite (TRMM) Relatively large error and use this data Sometimes Results Different from the measured data by Rain stations offers. 2. Best performance, sensor data, satellite (TRMM) in the southern half of the province and the maximum error deviation of the data relative to ground stations in the northern half of the province.    }, keywords = {Cluster analysis,Zoning,Precipitation,Sistan and Baluchistan,Satellite TRMM}, title_fa = {مقایسه پهنه‌های بارشی استان سیستان و بلوچستان با استفاده از داده‌های ماهواره‌ای و ایستگاه‌های زمینی}, abstract_fa = {برایپهنه بندیبارش در استانسیستانوبلوچستاناز دو سریداده‌هایماهواره (TRMM)[1]1 با قدرت تفکیک 5/0*5/.0درجه وداده‌های145 ایستگاه باران سنجی‌، اقلیم شناسی و ایستگاه‌های سینوپتیک براییکدوره12ساله (1378-1390)استفادهگردید. برایاینتقسیمبندی بر روی هر دو سری داده‌هااز طریق تحلیل مولفه‌های اصلی وتحلیلخوشه‌ایبهروشوارداعمال گردید. با بررسی به عمل آمده بر روی ماتریس بارگویه‌ها و خوشه‌بندی داده‌های ایستگاهی استان به سه پهنه کم بارش وسیع،متوسط بارش مکرانی و پهنه پر بارش سراوانی تقسیم گردید.نتیجه تحلیل مولفه‌های اصلی و خوشه‌بندی بارش با داده‌های ماهواره (TRMM)،تقسیماستانبهسهپهنهپر بارش شمالی، کم بارش مرکزی و پهنه متوسط بارش جنوبی می‌باشد. مقایسه پهنه بندی با دو سری داده مذکور نشان می‌دهد که پهنه‌بندی با داده‌های ماهواره (TRMM) دارای خطای بالایی در تقسیم بندی بارشی استان می‌باشد بطوری که ناحیه شمالی وغربی استان را که دارای ارتفاع کم از سطح دریا و فاصله بیشتری از منابع رطوبت می‌باشند را منطقه‌ای با بیشترین میزان بارش نشان داده و همچنین ناحیه مرکزی که درآن رشته کوه‌های مکران واقع شده و در تابستان از سامانه‌های موسمی هند بهره‌مند است را به عنوان کم بارش ترین ناحیه استان معرفی می‌‎کند. موارد مذکور در پهنه بندی با داده‌های باران سنجی لحاظ شده است و این پهنه بندی مبنای مناسب تری جهت استفاده در برنامه‌ریزی می‌باشد. نقطه قوت دو پهنه بندی در یکسان بودن رژیم بارندگی می‌باشد.   1. Tropical Rainfall Measuring Mission}, keywords_fa = {تحلیل خوشه‌ای,پهنه بندی,بارش,سیستان و بلوچستان,ماهواره (TRMM)}, url = {https://clima.irimo.ir/article_14152.html}, eprint = {https://clima.irimo.ir/article_14152_3196f2a5d72bed079e92f602d5affd40.pdf} } @article { author = {Ghasemi, Elham and Fattahi, Ebrahim and Babaiei, O}, title = {Estimating the Impact of Climate change on Snowmelt Runoff on Feature}, journal = {Journal of Climate Research}, volume = {1392}, number = {13}, pages = {111-122}, year = {2013}, publisher = {https://www.irimo.ir/}, issn = {2228-5040}, eissn = {2783-395X}, doi = {}, abstract = {Introduction According to the Intergovernmental Panel on Climate Change (IPCC), mean global surface temperature increased 0.6 ± 0.2ºC in the 20th century. Snow cover decreased by 10% since the late 1960’s. General Circulation Models (GCMs) are an important tool in the assessment of climate change. These numerical coupled models represent various earth systems including the atmosphere, oceans, land surface and sea-ice and offer considerable potential for the study of climate change and variability (H. J. Fowler). The spatial resolutions of GCMs are about 1.1° or 4° in latitude and from 1.1° to 5° in longitude. Tether for, major problems in using the output of GCM is their low degree of resolution and large scaling. So to make them appropriate for use, downscaling methods have been developed to overcome this problem. ClimGendownscling method; ClimGen combines GCM-resolution climate change data derived from the pattern scaling method at a 5 degree resolution with observations of climate at half –degree resolution to simulate future climates at half-degree resolution contains a database of outputs from GCMs and currently produces 8 climate variables on a 0.5 x 0.5 degree grid: temperature (max1, mean and min), precipitation, vapor pressure, cloud cover and wet-day frequency. For each month, season, or annually. Materials and Methods The climate change scenario data used in this study are based on simulations carried out using General Circulation Models (GCMs) for the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC, 2007). For our assessment we used simulations of monthly temperature and precipitation over the period 2010-2099 carried using the climate model HADCM3 and future projections in the context of the A1B scenario. The A1B scenario describes a world of very rapid economic growth, global population that peaks in the mid-21st Century and declines thereafter, and the rapid introduction of new and more efficient technologies. The Snowmelt Runoff Model (SRM (is one of the applied models for simulation and forecasting of Snowmelt runoff in mountainous areas where snowmelt is a major runoff factor. This model based on the Degree-Day factor, which is used to simulate snowmelt runoff during the melt season. The input parameters for the model are derived from satellite data, metrological and hydrological data. In this study, MODIS-8 daily (MOD10A2) snow cover products are used for snow cover maps (2006). Topographic Mission (SRTM) data is used to obtain the digital elevation model (DEM) of the region and to create different elevation zones. The Normalized Difference Snow Index (NDSI) algorithm is applied with the reflectance of band 4 and band 6 for snow cover mapping and to differentiate the snow from other land features. Because The NDSI is a useful tool to calculate the snow covered area. The Snow Cover Depletion Curve (SCDC) is made using the DEM and snow cover maps to get daily percent values of snow covered area. The SCDC is an important variable for the SRM simulations. Other variables (temperature and precipitation) and parameters (degree-day factor, recession coefficient, runoff coefficients, time lag, critical temperature and temperature lapse rate) are used as input to the SRM model for snowmelt simulation. In order to analyze climate change impacts on Snowmelt by HadCM3 model, we compared the simulated values for the baseline period 1961-1990 with the values for 7 periods in the future: (2010-2039….2070-2099) based ClimGen model. Results Mean monthly temperature and precipitation for the base period (1961 - 1990) with the observed data at stations in the same period, the study was conducted and the results showed that the model simulated temperature and precipitation ClimGen is acceptable. Climate change scenarios of temperature and precipitation in the region shows that the temperature rise and changes in precipitation in the basin in the coming years is fluctuating. Snow melt runoff in the month of January to December 2006 was simulated. The results show value of (R2) is 0.71 and the volume difference DV is 0.45 %. Then consider scenarios of climate change and changes in the values of snow cover, runoff simulations were performed for the future. Conclusion For study the effects of climate change on river flow and snow cover in the study area use SRM model. Result shows Due to the increased temperatures and increased snow melt, because of reduced snow cover, basin runoff does not increase.    }, keywords = {general circulation models,Climate change,runoff,SRM}, title_fa = {بررسی نوسانات روانآب حاصل از ذوب برف تحت تأثیر پدیده تغییر اقلیم در دهه‌های آینده}, abstract_fa = {    با توجه به گسترش رشته کوه‌های زاگرس در بخش‌های غرب و جنوب غرب کشور، پوشش برفی این رشته کوه‌ها یکی از منابع بزرگ تامین آب در حوزه‌های آّبریز دز، ‌کرخه و کارون می‌باشد این در حالی است که تغییر در سطح پوشش برف در دهه‌های آینده می‌تواند روانآب حاصل از ذوب برف که نقش عمده‌ای در تأمین پتانسیل آبی در این مناطق را دارا می‌باشد، دستخوش تغییراتی قرار دهد. در پژوهش حاضر نوسان روانآب حاصل از ذوب برف تحت تأثیر تغییرات دما و بارش با استفاده از مدل SRM در دوره‌های 2020 (2010-2039)، 2030 (2020-2049)، 2040 (2030-2059)، 2050 (2040-2069)، 2060 (2050-2079)، 2070 (2060-2089) و 2080 (2070-2099) با بهره‌گیری از برونداد مدل HadCM3 از مرکز تحقیقات و پیش بینی اقلیمHadley تحت سناریوی A1B در حوضه آبریز بختیاری مورد بررسی قرار گرفته است. در این مطالعه دوره آماری 1961-1990دوره اقلیمی پایه در نظر گرفته شده است و سری زمانی داده‌های مشاهداتی دما و بارش برای ایستگاه‌های منطقه مورد مطالعه در دوره مذکور جمع آوری گردیده ‌است. برای پیش بینی متغیرهای اقلیمی از مدل‌های گردش عمومی جو (General Circulation Model) مدل  HadCM3انتخاب و برونداد‌های این مدل برای متغیرهای دما و بارش در مقیاس (5/0 * 5/0 درجه) از پایگاه داده‎های CLIMGEN با سناریو انتشارA1B  برای هر دوره سی‌ساله 2020 (2010-2039) تا 2080 (2070-2099) استخراج و تحلیل شده است. سپس با اعمال سناریوهای تغییر اقلیم و تغییرات سطح پوشش برف، شبیه‎سازی روانآب با استفاده از مدلSRMدر دوره‌های یاد شده ارزیابی گردیده است. نتایج بدست آمده از شبیه‌سازی، کاهش حجم روانآب در دوره‎های یاد شده را نشان می‌دهد.      }, keywords_fa = {مدل‌های گردش عمومی جو,تغییر اقلیم,روانآب,SRM}, url = {https://clima.irimo.ir/article_14153.html}, eprint = {https://clima.irimo.ir/article_14153_8870f62ee62e5d5e2344821f014130a1.pdf} } @article { author = {Sadeghi, E. and Mashhadi Hossainali, Masoud}, title = {Application of Numerical Weather Prediction Models to Improve the Accuracy of Satellite Positioning Method}, journal = {Journal of Climate Research}, volume = {1392}, number = {13}, pages = {123-131}, year = {2013}, publisher = {https://www.irimo.ir/}, issn = {2228-5040}, eissn = {2783-395X}, doi = {}, abstract = {Introduction The remarkable advantage of Global Navigation Satellite Systems (GNSS) is providing navigation service to the user communities, which is independent of the weather condition. The GNSS signals pass through different layers of the Earth's atmosphere. Propagating signals are refracted while passing through every layer and therefore, the reception of the signals is delayed. This delay distorts the accuracy of the position which is computed for a receiver. Troposphere, which is the lower most part of the Earth's atmosphere, is one of the most important source of bias in this respect. To analyze the impact of tropospheric delay, it is normally divided into dry and wet components. The contribution of the dry and wet parts is reported to be 90 and 10 percent respectively. In contrary to the wet component, the existing models are precise enough to compute the contribution of the dry part on the signal delay. Therefore, analyzing the efficiency of numerical weather models for modeling the tropospheric delay as compared to the existing standard techniques seems to be remarkable. Materials and methods In this paper, three methods are used for computing the tropospheric error. The first method is based on the Saastamoinen's global troposphere model. This model is commonly used for computing the tropospheric error. Required input parameters are derived from the standard atmosphere model. In the second and third method, ray tracing is used for correcting the GPS measurements. A Numerical Weather Prediction (NWP) model is used for this purpose. Here, meteorological data measured at the position of the station are used as the input parameters of the model. In the third method, the required surface meteorological data are extracted from a NWP model. The World Research and Forecasting (WRF) model is used for this purpose. The WRF daily forecasts with a horizontal resolution of 0.1 degrees in 25 pressure levels (from 1000 to 50 mill bars) are used together with the GPS carrier phase and code measurements at station TKBN. This permanent GPS station is located at ϕ=36̊ 47ˊ 9.33˝ and λ=50̊ 55ˊ 48.20˝. Sampling rate of the GPS measurements is 30 seconds. The observation time interval starts at November 2 and ends at November 13, 2011. Results and discussion Computed tropospheric corrections are applied to the raw measurements above. Daily precise positions of this station are estimated using the corrected data. Repeatability of the point positions is used as a measure for analyzing and comparing the obtained results. The repeatability of the station coordinates in the north component increases from 3.13 mm in the first method to 0.98 mm in the second one. The repeatability of the east and north components also improves by 1.73 mm and 2.11mm respectively when the raw observations are corrected by the tropospheric corrections which are derived from the second method above. This proves the efficiency the WRF numerical weather model for estimating the tropospheric error when the surface meteorological data that is observed at station is used. Conclusion The use of ray tracing with surface data from WRF model in comparison with Saastamoinen model does not lead to improved repeatability coordinates in all three components. The results of this study emphasize the Numerical Weather Prediction models used in this study has been poor to calculate the surface data. Otherwise the results of the ray tracing method based on WRF model with surface meteorological data is observed at station is the best method for Computed tropospheric corrections.        }, keywords = {Global Positioning System,precise Point position,Numerical Weather Prediction,ray tracing,global troposphere model}, title_fa = {کاربرد مدل‌های عددی پیش‌بینی وضع هوا در بهبود دقت تعیین موقعیت به روش ماهواره‌ای}, abstract_fa = {ارائه خدمات ناوبری در تمامی شرایط آب و هوایی به کاربران، همواره به عنوان یکی از مزایای بسیار برجسته سیستم‌های تعیین موقعیت ماهواره‌ای به حساب می‌آید. امواج ارسالی از ماهواره‌های‌این سیستم‌ها از لایه‌های مختلف جو عبور کرده و‌این امر منجر به‌ایجاد انکسار در مسیر حرکت موج و در نهایت تأخیر در دریافت امواج مذکور می‌گردد.‌این تأخیر به برآوردی ناصحیح از موقعیت گیرنده منجر می‌شود. از آنجا که بیشتر فرآیند های جوی در پایینی‌ترین لایه اتمسفر زمین (تروپوسفر) رخ می‌دهد،‌این لایه، یکی از منابع مهم‌ایجاد خطا در تعیین موقعیت مطلق دقیق (PPP[1]) با سیستم تعیین موقعیت جهانی ((GPS[2] است. از روش‌های مختلفی برای تعامل با‌این منبع خطا استفاده می‌شود. در‌این مقاله از مدل پیش‌بینی جهانی سستامینن[3] که با داده‌های حاصل از مدل استاندارد اتمسفری حمایت می‌شود و روش ردیابی اشعه[4] با استفاده از مدل پیش‌بینی عددی وضع هوا[5]، مدل پیش‌بینی و تحقیقاتی آب و هوا (WRF)[6] جهت برآورد تأخیر مایل تروپوسفری استفاده گردیده است. نتایج در سطح تعیین موقعیت مقایسه و بررسی شده است. موقعیت مطلق دقیق یک نقطه پس از حذف خطای مورد بحث از هر دو روش در یک بازه دوازده روز تعیین گردید. جهت بررسی نتایج از روش تکرار پذیری استفاده شده است. تکرار پذیری در مؤلفه ارتفاعی نتایج حاصل از تصحیح تأخیر تروپوسفر با استفاده از مدل سستامینن و ردیابی اشعه به ترتیب 13/3 میلی متر و 98/0 میلی‌متر به دست آمده است. کاهش 15/2 میلی متر در خطای مربعی متوسط نمایانگر پتانسیل مدل‌های عددی پیش‌بینی وضع هوا جهت دستیابی به برآوردی صحیح‌تر در مقدار تخمین تأخیر تروپوسفری با استفاده از روش ردیابی اشعه است.     [1]. Principal point positioning [2]. Global Positioning System (GPS) [3] .Saastamonien [4]. Ray tracing [5]. Numerical Weather Prediction Models(NWP) 7. Weather Research and Forecasting (WRF)  }, keywords_fa = {سیستم تعیین موقعیت جهانی,تعیین موقعیت مطلق دقیق,مدل عددی پیش­بینی وضع هوا,ردیابی اشعه}, url = {https://clima.irimo.ir/article_14154.html}, eprint = {https://clima.irimo.ir/article_14154_7a94c190d80980b24c7ef2591490cf1d.pdf} }