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واکاوی ساختار دمای ایران مبتنی بر برون‌داد پایگاه دادۀ مرکز پیش‌بینی میان‌مدت هواسپهر اروپایی (ECMWF) نسخۀ ERA Interim | ||
| پژوهش های جغرافیای طبیعی | ||
| مقاله 11، دوره 50، شماره 2، تیر 1397، صفحه 353-372 اصل مقاله (1.71 M) | ||
| نوع مقاله: مقاله کامل | ||
| شناسه دیجیتال (DOI): 10.22059/jphgr.2018.238512.1007092 | ||
| نویسندگان | ||
| محمود احمدی* 1؛ عباسعلی داداشی رودباری2؛ حمزه احمدی3؛ زهرا علی‌بخشی2 | ||
| 1دانشیار آب و هواشناسی دانشگاه شهید بهشتی، دانشکدة علوم زمین، تهران، ایران | ||
| 2دانشجوی دکتری آب و هواشناسی شهری، دانشگاه شهید بهشتی، دانشکدة علوم زمین، تهران، ایران | ||
| 3دانشجوی دکتری آب و هواشناسی کشاورزی، دانشگاه حکیم سبزواری، دانشکدة جغرافیا و علوم محیطی، سبزوار، ایران | ||
| چکیده | ||
| هدف از این پژوهش پایشِ دمایِ هوا با رهیافتی آماری بر اساس برون‌داد پایگاه دادة بازواکاویشده (ECMWF) نسخة ERA Interim برای دورة زمانی 1979ـ2015 با تفکیک مکانی 125/0×125/0 درجة قوسی و همسنجی آن با پایگاه ملی اسفزاری و پیمونگاههای همدید کشور است. از سنجههای RMSE و R2 برای اعتبارسنجی نتایج و از بُعد فرکتالی برای دگردیسی زمانی استفاده شد. نتایج اعتبارسنجی پایگاه ECMWF نشاندهندة توانایی و دقت زیاد آن در برآورد دمای هوا بوده است. همسنجی پایگاه ECMWF با پایگاه ملی اسفزاری و پیمونگاههای همدید از نتایج مطلوبی برخوردار است؛ این نتایج در ششماهة دوم یا نیمة گرم سال، به سبب تباین کمتر دمایی، از نتایج بسیار مطلوبتری برخوردار است. بُعد فرکتالی دما در دورة گرم سال (دارای دگرگونی کوتاهمدت) افزایش یافته و در دورة سرد سال (دارای دگرگونی بلندمدت) کاهش یافته است. توزیع فضایی دمای هوا نشان داد در ایجاد الگوی دمای هوا در بام ایران عامل عرض جغرافیایی بیشترین نقش را ایفا میکند. کانون گرمترین نواحی در مناطقِ کویر لوت، جنوبشرق، و نوار جنوب کشور است. مناطق خنک و سرد نیز منطبق بر نواحی مرتفع و پیکربندی ناهمواریهای بام ایران است. | ||
| کلیدواژه‌ها | ||
| ایران؛ بُعد فرکتالی؛ پایگاه دادة بازواکاویشده؛ دمای هوا؛ .ECMWF ERA Interim | ||
| عنوان مقاله [English] | ||
| Analysis of Iran Temperature Structure Based on ECMWF, ERA Interim Version | ||
| نویسندگان [English] | ||
| Mahmoud Ahmadi1؛ Abbasali Dadashi Roudbari2؛ Hamzeh Ahmadi3؛ Zahra Alibakhshi2 | ||
| 1Associate Professor of Climatology, Shahid Beheshti University, Tehran, Iran | ||
| 2PhD candidate in Urban Climatology, Shahid Beheshti University, Tehran, Iran | ||
| 3PhD candidate in agricultural climatology, Hakim Sabzevari University, Iran | ||
| چکیده [English] | ||
| Introduction Air temperature is one of the most important climate measurements in the human environment, which directly affects the physical and biological processes of the ecosystem. Understanding this climate measure can be the basis for understanding many climatic processes, especially evapotranspiration (due to the climate of Iran). Reanalysis have been used in recent years in many studies, including the studies about climate trends, climate modeling, and the assessment of renewable resources, and their high accuracy. The purpose of this present study is to evaluate the accuracy of the open-baseline base temperature data of the European Centre for Medium-Range Weather Forecasts (ECMWF) of the ERA-Interim version with a 0.125 × 0.125 arc-spatial resolution in a survey with observational data from the weather stations and the National Bassoon database. In this regard, we have evaluated the temporal changes of the temperature of Iran. Materials and methods In this study, we have used data from 32 weather observation points during the statistical period of 1979-2015 to validate the ESFAZRI national Database. ERA-Interim, also produced by ECMWF, uses 4D-variational analysis on a spectral grid with triangular truncation of 255 waves (corresponds to approximately 80 km) and a hybrid vertical coordinate system with 60 levels. The ECMWF global model is used for the forward integration in the 4D-variational analysis and the temporal length of the variational window in 12 h. As stated for the validation of the temperature data of the ECMWF database of the ERA-Interim version, we verified the cells of this site with the data of the 32 well-selected stations in the period 1979-2015. The nearest cells were selected to be sampled. To verify the two data sets, the R2 and RMSE indices were used. In order to evaluate the changes in Iran's monthly temperature, fractal dimension was calculated. Results and discussion The results of the validation between the European Centre for Medium-Range Weather Forecasts (ECMWF) and the ERA-Interim Emission and Interim National Projections for the period (1979-2015) showed that this base has a high performance, as observed in most of the pioneering cases. In the section, more than 98% of the coefficient of determination between the data of this base is observed with the data observed and recorded in the observer weather stations of the country. In the following summer, the fractal has reached its maximum value, reaching 1.63 in August for its growth over the year. Accordingly, the fractal dimension is increased and this increase reflects short-term variations, which means that the standard error also increases. In the cold period of the next year, fractals showed a decrease in value, reflecting short-term changes. In the cold season from December to March, the average temperature varies from 7.2 to 7.3. The minimum air temperature varies from -3 to -6 degrees, and the maximum air temperature from 21.4 to 23.0 degrees. The dispersion of the December temperature values is more than that in January and February. The data distribution was observed positively at 0.51 in December, 0.41 in January, and 0.30 in December. In the cold months, the temperature distribution is more positive and, in fact, the values are less than the average values of a higher frequency. The average temperature in June was 28.2, 30 and 29 degrees Celsius. The range of changes in June, July and August was 22.3, 20.3 and 19.6, and the temperature diffraction in these months was also 27.9, 20.19 and 18.4, respectively. The range of changes and dispersion in the months of July and August is higher than that in September. Conclusion This study evaluated the mean air temperature based on the ERA Interim version of the European Centre for Medium-Range Weather Forecasts (ECMWF) data model. The results showed that the model was able to measure the temperature in the long run. The average of air temperature in all months of the year with the spatial component of the latitude has the highest correlation coefficient. The fractal dimension of the air temperature in the cold months is less than the warm months of the year. The highest fractal dimension occurs in the months of July and August coincident with the warmest periods of the year. This indicates short-term changes due to the stability of the systems in the warm period of the year and long-term changes due to the variety of macro-scale systems in the cold period of the year. This statistic for Iran's temperature indicated that the climate and, more specifically, temperature are a complex and non-linear system composed of different measures and interactions. According to the results, it can be stated that the southern regions of the country on the coast of Oman Sea and the Persian Gulf and the northern Persian Gulf in Khuzestan province require to consider the days of cooling demand in the warm months of the year in order to adjust the air temperature for providing comfort in these areas. On the other hand, the pattern map for each month showed that the Northwest, high Zagros and Northeastern regions required more attention in terms of heating in the cold months of the year. | ||
| کلیدواژه‌ها [English] | ||
| reanalysis database, ECMWF, Air temperature, fractal dimension, Iran | ||
| مراجع | ||
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اکبری، ط. و مسعودیان، ا. (1388). شناسایی رژیم دمایی و پهنهبندی نواحی دمایی ایران، جغرافیاوبرنامهریزیمحیطی، 20(1): 59-74. حلبیان، ا. و شبانکاری، م. (1391). نقش پُرفشار سیبری در پراکنش دماهای کمینة روزانة ایران، جغرافیاوبرنامهریزیمحیطی، 23(4): 151-166. دارند، م. (1394). واکاوی وردایی زمانی- مکانی رطوبت جوی ایرانزمین طی بازة زمانی 1979-2013، پژوهشهایجغرافیایطبیعی، 47(2): 213-239. درگاهیان، ف.؛ علیجانی، ب. و محمدی، ح. (1393). شناسایی الگوی همدیدی سامانههای بندالی مؤثر بر دمای ایران، نشریة پژوهشهایاقلیمیشناسی، 5(19 و 20): 81-92. رضیئی، ط. (1396). شناسایی رژیمهای دمایی ایران با استفاده از روشهای چندمتغیره، مجلة ژئوفیزیکایران، 11(2): 15-35. سروزاده، ک. و اشتیاقی، ع. (1390). تئوریشبکهایوشهرفرکتال (دیدگاههاینویندرزمینةبرنامهریزیشهری)، شیراز: نوید شیراز. علیجانی، ب. (1389). آبوهوایایران، چ 10، تهران: انتشارات دانشگاه پیام نور. غیور، ح. و منتظری، م. (1383). پهنهبندی رژیمهای دمایی ایران با مؤلفههای مبنا و تحلیل خوشهای، مجلة جغرافیاوتوسعه، 2: 21-34. قرخلو، م. و زنگنه شهرکی، س. (1388). شناخت الگوی رشد کالبدی- فضای شهر با استفاده از مدلهای کمّی، مجلة جغرافیاوبرنامهریزیمحیطی، 20(2): 19-40. مسعودیان، س.ا.؛ زینالی، ح. و حجتیزاده، ر. (1387). نواحی دمایی ایران، تحقیقاتجغرافیایی، 23(2): 3-18. مسعودیان، س.ا. (1382). تحلیل ساختار دمای ماهانة ایران، مجلةپژوهشیدانشگاهاصفهان، 15(1 و 2): 87-96. مسعودیان، س.ا. (1383). بررسی روند دمای ایران در نیم سدة گذشته، مجلة جغرافیاوتوسعه، 37(54): 29-45. مسعودیان، س.ا. (1390). آبوهوایایران، مشهد: شریعة توس مشهد. منتظری، م. (1393). نواحی حرارتی ایران، فصلنامة تحقیقاتجغرافیایی، 29(2): 135-144. میرکتولی، ج.؛ بارگاهی، ر. و عقیلی، ز. (1393). تبیین ابعاد استفاده از هندسة فراکتال در تحلیلهای جغرافیا و برنامهریزی شهری، مجلة آمایشجغرافیاییفضا، 4(14): 55-82. Akbari, T. and Masoudian, A. (2009). Identification of temperature regime and zoning of temperature regions of Iran, Geography and environmental planning, 20 (1): 59-74 (in Persian). Alijani, B. (2010). Climate Iran, Payam Noor University Press, 10 Edition, Tehran, 221p (in Persian). Bao, X. and Zhang, F. (2013). Evaluation of NCEP–CFSR, NCEP–NCAR, ERA-Interim, and ERA-40 reanalysis datasets against independent sounding observations over the Tibetan Plateau, Journal of Climate, 26(1): 206-214. Bengtsson, L.; Hodges, K.I. and Hagemann, S. (2004). Sensitivity of large‐scale atmospheric analyses to humidity observations and its impact on the global water cycle and tropical and extratropical weather systems in ERA40, Tellus A, 56(3): 202-217. Center, C.P. (2008). National Centers for Environmental Prediction (NCEP), National Oceanic and Atmospheric Administration (NOAA), United of States Department of Commerce (USDA). Ciccarelli, N., Von Hardenberg, J., Provenzale, A., Ronchi, C., Vargiu, A., & Pelosini, R. (2008). Climate variability in north-western Italy during the second half of the 20th century. Global and Planetary Change, 63(2-3), 185-195. Darand, M. (2015). Analysis of Spatio-Temporal Variation of Atmospheric Humidity in Iran during 1979-2013. Physical Geography Research Quarterly, 47(2): 213-239. doi: 10.22059/jphgr.2015.54460 (in Persian). Dargahian, F.; Alijani, B. and Mohammadi, H. (2014). Identification of the Pattern of Blocking System Affecting Iran's Temperature, Journal of Climate Research, 5(19 and 20): 81-92 (in Persian). Ebita, A.; Kobayashi, S.; Ota, Y.; Moriya, M.; Kumabe, R.; Onogi, K. ... and Kamahori, H. (2011). The Japanese 55-year reanalysis “JRA-55”: an interim report. Sola, 7: 149-152. Eliasson, I. and Upmanis, H. (2000). Nocturnal airflow from urban parks-implications for city ventilation, Theoretical and Applied Climatology, 66(1): 95-107. Fllah Ghalhari, G.F. and Dadashi Roudbari, A.D. (2016). An investigation on thermal patterns in Iran based on spatial autocorrelation, Theoretical and Applied Climatology, 1-12. Frauenfeld, O.W.; Zhang, T. and Serreze, M.C. (2005). Climate change and variability using European Centre for Medium‐Range Weather Forecasts reanalysis (ERA‐40) temperatures on the Tibetan Plateau, Journal of Geophysical Research: Atmospheres, 110(D2). Gayour, H. and Montazarei, M. (2004). Classification of Temperature Regime of Iran Using PCA and CA., Geography and Development Iranian Journal, 2(4): 21-34. doi: 10.22111/gdij.2004.3882 (in Persian). Halabian, A. and Shabankari, M. (2013). The role of Siberian high in distribution of the daily minimum temperatures in Iran, Geography and Environmental Planning, 23(4): 151-166 (in Persian). Henfridsson, U., Neimane, V., Strand, K., Kapper, R., Bernhoff, H., Danielsson, O., ... & Bergman, K. (2007). Wave energy potential in the Baltic Sea and the Danish part of the North Sea, with reflections on the Skagerrak. Renewable Energy, 32(12), 2069-2084. Hodges, K.I.; Lee, R.W. and Bengtsson, L. (2011). A comparison of extratropical cyclones in recent reanalyses ERA-Interim, NASA MERRA, NCEP CFSR, and JRA-25, Journal of Climate, 24(18): 4888-4906. Kalnay, E. (2003). Atmospheric modeling, data assimilation and predictability, Cambridge University Press. Kalnay, E.; Kanamitsu, M.; Kistler, R.; Collins, W.; Deaven, D.; Gandin, L. ... and Zhu, Y. (1996). The NCEP/NCAR 40-year reanalysis project, Bulletin of the American meteorological Society, 77(3): 437-471. Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S. K., Hnilo, J. J., Fiorino, M., & Potter, G. L. (2002). Ncep–doe amip-ii reanalysis (r-2). Bulletin of the American Meteorological Society, 83(11), 1631-1644. Kanamitsu, M.; Ebisuzaki, W.; Woollen, J.; Yang, S.K.; Hnilo, J.J.; Fiorino, M. and Potter, G.L. (2002). Ncep–doe amip-ii reanalysis (r-2), Bulletin of the American Meteorological Society, 83(11): 1631-1643. Kharghloo M. and Zanganeh Shahraki, S. (2009).Understanding the Pattern of City-Based Growth by Using Quantitative Models, Geography and Environmental Planning, 20(2): 19-40 (in Persian). Kiehl, J.T.; Schneider, T.L.; Rasch, P.J.; Barth, M.C. and Wong, J. (2000). Radiative forcing due to sulfate aerosols from simulations with the National Center for Atmospheric Research Community Climate Model, Version 3, Journal of Geophysical Research: Atmospheres, 105(D1): 1441-1457. Kim, H.M.; Webster, P.J. and Curry, J.A. (2012). Seasonal prediction skill of ECMWF System 4 and NCEP CFSv2 retrospective forecast for the Northern Hemisphere Winter, Climate Dynamics, 39(12): 2957-2973. Krishna Kumar, K.; Hoerling, M. and Rajagopalan, B. (2005). Advancing dynamical prediction of Indian monsoon rainfall, Geophysical Research Letters, 32(8). Kug, J.S.; Kang, I.S. and Choi, D.H. (2008). Seasonal climate predictability with tier-one and tier-two prediction systems, Climate dynamics, 31(4): 403-416. Lader, R.; Bhatt, U.S.; Walsh, J.E.; Rupp, T.S. and Bieniek, P.A. (2016). Two-meter temperature and precipitation from atmospheric reanalysis evaluated for Alaska, Journal of Applied Meteorology and Climatology, 55(4): 901-922. Lashkari, A.; Bannayan, M.; Koocheki, A.; Alizadeh, A.; Choi, Y.S. and Park, S.-K. (2016). Applicability of AgMERRA forcing dataset forgap-filling of in-situ meteorological observation, Case Study: Mashhad Plain, Journal of Water and Soil, 29(6): 1749-1758 (In Persian). Li, S.; Xie, Y.; Brown, D.G.; Bai, Y.; Hua, J. and Judd, K. (2013). Spatial variability of the adaptation of grassland vegetation to climatic change in Inner Mongolia of China, Applied Geography, 43: 1-12. Masoudian, A.S. (2003). Monthly Temperature Structure Analysis of Iran, Research Journal of Isfahan University, 15(1 and 2): 87-96 (In Persian). Masoudian, A.S. (2004). Trend of temperatures in Iran the last half century, Journal of Geography and Development, 37(54): pp. 29-45 (in Persian). Masoudian, A.S. (2011). Iranian Climate, Sharia Toos Publishing, Mashhad, First Printing, Mashhad, 288 p. (In Persian). Masoudian, A.S.; Zeinali, H. and Hojjatizadeh, R. (2008). Iranian Temperature Areas, Geographic Research, 23(2): 3-18 (in Persian). Mirkatouli, J.; Bargahi, R. and aghili, S.Z. (2014). Explanation of Fractal Geometry in Geography and Urban Planning, Geographical Planning of Space, 4(14): 55-82 (In Persian). Montazeri, M. (2014). Thermal Regions of Iran, Geores, 29(2) :135-144 (In Persian). Mooney, P.A.; Mulligan, F.J. and Fealy, R. (2011). Comparison of ERA‐40, ERA‐Interim and NCEP/NCAR reanalysis data with observed surface air temperatures over Ireland, International Journal of Climatology, 31(4): 545-557. Nikos Angelos Salingaros (2010). Network Theory and Fractal Town (New Approaches to Urban Planning), Translate By: Sid Kourosh Sarangzadeh and Alireza Eshatighi, Islamic Azad University, Jahrom Branch, Navid Shiraz Publishers,144p (In Persian). Poveda, G., Waylen, P. R., & Pulwarty, R. S. (2006). Annual and inter-annual variability of the present climate in northern South America and southern Mesoamerica. Palaeogeography, Palaeoclimatology, Palaeoecology, 234(1), 3-27. Raziei, T. (2017). Identification of the temperature regimes of Iran using multivariate methods, Iranian Journal of Geophysics, 11(2): 15-35 (in Persian). Rienecker, M.M.; Suarez, M.J.; Gelaro, R.; Todling, R.; Bacmeister, J.; Liu, E. ... and Bloom, S. (2011). MERRA: NASA’s modern-era retrospective analysis for research and applications, Journal of climate, 24(14): 3624-3648. Rolland, C. (2003). Spatial and seasonal variations of air temperature lapse rates in Alpine regions, Journal of Climate, 16(7): 1032-1046. Serv Zadeh, K. and Eshtiaghi, A.S. (2011). Network Theory and Fractal City (New Approaches to Urban Planning), Shiraz Navid Publications, Shiraz, 144 p (In Persian). Simmons, A. (2006). ERA-Interim: New ECMWF reanalysis products from 1989 onwards, ECMWF newsletter, 110: 25-36. Simmons, A.J.; Poli, P.; Dee, D.P.; Berrisford, P.; Hersbach, H.; Kobayashi, S. and Peubey, C. (2014). Estimating low‐frequency variability and trends in atmospheric temperature using ERA‐Interim, Quarterly Journal of the Royal Meteorological Society, 140(679): 329-353. Simmons, A.J.; Willett, K.M.; Jones, P.D.; Thorne, P.W. and Dee, D.P. (2010). Low‐frequency variations in surface atmospheric humidity, temperature, and precipitation: Inferences from reanalyses and monthly gridded observational data sets, Journal of Geophysical Research: Atmospheres, 115(D1). Smith, C.A.; Compo, G.P. and Hooper, D.K. (2014). Web-Based Reanalysis Intercomparison Tools (WRIT) for analysis and comparison of reanalyses and other datasets, Bulletin of the American Meteorological Society, 95(11): 1671-1678. Stahl, K.; Moore, R.D.; Floyer, J.A.; Asplin, M.G. and McKendry, I.G. (2006). Comparison of approaches for spatial interpolation of daily air temperature in a large region with complex topography and highly variable station density, Agricultural and Forest Meteorology, 139(3): 224-236. Terzi, F. and Kaya, H.S. (2008). Analyzing urban sprawl patterns through fractal geometry: The case of Istanbul metropolitan area. Trisurat, Y.; Shrestha, R.P. and Kjelgren, R. (2011). Plant species vulnerability to climate change in Peninsular Thailand, Applied Geography, 31(3): 1106-1114. Wang, A. and Zeng, X. (2012). Evaluation of multireanalysis products with in situ observations over the Tibetan Plateau, Journal of Geophysical Research: Atmospheres, 117(D5). | ||
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