واکاوی ساختار دمای ایران مبتنی بر برون‌داد پایگاه دادۀ مرکز پیش‌بینی میان‌مدت هواسپهر اروپایی (ECMWF) نسخۀ ERA Interim

نوع مقاله: مقاله علمی پژوهشی

نویسندگان

1 دانشیار آب‏ و هواشناسی دانشگاه شهید بهشتی، دانشکدة علوم زمین، تهران، ایران

2 دانشجوی دکتری آب‏ و هواشناسی شهری، دانشگاه شهید بهشتی، دانشکدة علوم زمین، تهران، ایران

3 دانشجوی 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

موضوعات


عنوان مقاله [English]

Analysis of Iran Temperature Structure Based on ECMWF, ERA Interim Version

نویسندگان [English]

  • Mahmoud Ahmadi 1
  • Abbasali Dadashi Roudbari 2
  • Hamzeh Ahmadi 3
  • Zahra Alibakhshi 2
1 Associate Professor of Climatology, Shahid Beheshti University, Tehran, Iran
2 PhD candidate in Urban Climatology, Shahid Beheshti University, Tehran, Iran
3 PhD 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
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