ارتباط بین تیپ الگوهای گردشی تراز دریا، با بارش‌های فراگیر در ایران

نوع مقاله: مقاله علمی پژوهشی

نویسندگان

1 دانشیار گروه جغرافیای طبیعی، دانشکدۀ جغرافیا، دانشگاه تهران، ایران

2 دانشجوی دورۀ دکتری اقلیم‎شناسی، دانشکدۀ جغرافیا، دانشگاه تهران، ایران

چکیده

در این مقاله ارتباط بین الگوهای جوی تراز دریا با بارش‌های فراگیر در ایران شناسایی شده است. برای این هدف از رویکرد همدید محیطی به گردشی استفاده شد؛ به‎طوری که ابتدا داده‎های بارش روزانۀ 210 ایستگاه همدید از سازمان هواشناسی ایران در دورۀ 1980 تا 2009 گرداوری شد. سپس با روش زمین‎مرجع کریجینگ، داده‎ها با ابعاد 9/5×9/5 کیلومتر میان‎یابی شد و با اعمال شرایطی، 1548 روز بارش فراگیر در ایران به‎دست آمد. در مرحلۀ دوم داده‌های روزانۀ میانگین فشار تراز دریا از سری داده‌های بازکاوی‎شدۀ NCEP/NCAR در 1548 روز مورد نظر برداشت شد. سپس با روش تحلیل مؤلفۀ مبنا (PCA) و تحلیل خوشه‎ای، الگوهای گردشی تراز دریا طبقه‎بندی شدند. درنتیجه پنج تیپ عمده برای بارش‎های فراگیر به‎دست آمد. نتایج نشان داد که بارش‎های فراگیر و سنگین ایران را می‎توان در اثر تقویت سه سامانۀ عمده در تراز دریا شناسایی کرد. این سامانه‎ها پرفشار تبت، پرفشار اقیانوس اطلس شمالی و کم‎فشار ایسلند ـ قطبی هستند. چنانچه این سه سامانه با هم تقویت شوند، گرادیان فشار روی ایران افزایش می‎یابد و با تشکیل یک جو باروکلینیک، بارش‎های فراگیر و سنگینی در ایران رخ می‌دهد که بیشینۀ میانگین آنها روی نواحی مرتفع کوه‎های زاگرس است.

کلیدواژه‌ها


عنوان مقاله [English]

The Relationship between Circulation Pattern Types in Sea Level Pressure and Precipitation in Iran

نویسندگان [English]

  • Ghasem Azizi 1
  • Teimour Alizadeh 2
1 Associate Prof. in Climatology, Faculty of Geography, University of Tehran, Iran
2 PhD Candidate in Climatology, Faculty of Geography, University of Tehran, Iran
چکیده [English]

Introduction
The recent developments in computer sciences have considerably affected the application of
new methods in climatology. Especially these new technologies have increased the usage of the
new methods in climatic classification. The previous classifications were calculated only based
on insufficient number of climatic factors. For example, the well-known classification of
Koppen was based on precipitation and temperature. In contrary to such threshold-based
classifications, the implementation of multivariate statistical techniques has allowed to classify
climate without predefined thresholds by grouping individual objects by Jacobeit (2010)
methodology. Application of multivariate analysis in climatology is conducted by Yarnal et al
(2001). The aim of this paper is to use the classification technique and recognize the circulation
patterns at the sea level and their connection to variability of precipitation in Iran. To obtain a
comprehensive view of the precipitation in Iran and its effective factors a number of the
researches are conducted. Many papers have investigated the main circulation and air masses as
effective factors on Iran precipitation. However, there is not an agreement among them and the
main disagreement seems to be about the methodology. Khoshhal (1997) using synoptic
analysis studied the greater than 100 mm precipitation in coastal area of Caspian Sea. He
showed that in contrast to the previous studies, the cold advection of the Siberian anticyclone
over Caspian Sea is not the main reason for forming the heavy precipitations and these events
are connected to the entrance and settlement of anticyclone and cyclonic systems. Applying the
Physical Geography Research Quarterly, 46 (3), Fall 2014 7
vorticity calculation, Alijani (2003) identified the rainy air masses in Tehran.He concluded that
the effect of 500 hPa level is stronger than other levels and the cyclonic circulation type create
the heavier precipitations.
Methodology
There are two main approaches in synoptic climatology: the environment to circulation and
circulation to environment approaches. Because of the high variability of precipitation,
researchers used the environment to circulation in their studies (Yarnal, 1993). As a result, the
environment to circulation approach is used in this paper as well. The mean daily precipitations
of synoptic stations of Iran were collected for time period of 1980 - 2009. The distributions of
these stations are shown in Fig 1. Then the point data were interpolated with cell size of
0.057° grid point (5.9􀵈5.9 Km). Totally a number of 46939 cells were calculated and an n 􀵈 p
matrix was created. Where n refers to the days (10958 days) and p refers to the spaces (46939).
Using this matrix in daily basis, the Percent area, Mean and maximum precipitation for all area
of Iran were calculated. To eliminate the local precipitation and considering only the extensive
precipitations, two conditions were defined: the average precipitation of Iran must exceed 1
mm, and over 40 percent of Iran area must receive precipitation. Accordingly, a number of 1548
days of extensive precipitation in the course of study area were recognized. For explanation of
the circulation patterns of these events, mean sea level pressure, in a scale of 2.5°􀵈2.5° grid
point, NCEP/NCAR reanalysis data from 0° to 100° eastern longitude and 10° to 80° north
latitude were collected and a 1548􀵈1189 matrix was created. The Principal Component
Analysis (PCA) was used in order to reduce the volume of the matrix. Many researchers have
used the PCA and its application in multivariate analysis.
Results and Discussion
The results of PCA over the extensive rain matrix of Iran are shown at table 1. As it can be seen
in the table, a number of 48 eigenvalues greater than one which explained 92% of the total
variance was obtained. Among these, 15 factors that explained more than 1% of whole the
variance were selected. These factors explained 88% of the total variance. Load factor matrix
score is the matrix that has a 154815 dimension.
Conclusion
In this paper, the connection between circulation patterns on sea level and Iran precipitation was
analyzed by applying environment to circulation approach. For this purpose, the daily grid point
precipitation with 5.9*5.9 Km dimension obtained 1548 days, with considering a condition that
at least precipitation in Iran must be 1 mm and also 40 percent of Iran area must receive the
precipitation. Sea level pressures of these days were selected for identification of the main type
of the circulation patterns. The (PCA) technique was used for reduction data and with cluster
analysis it obtained 5 main circulation pattern types.
The investigation of the relationship between the circulation patterns and the precipitation
8 Physical Geography Research Quarterly, 46 (3), Fall 2014
events revealed that there are five distinctive precipitation patterns in Iran. These types are
including:
Type 1: Interaction between Sudan low pressure and Siberian anticyclone;
Type 2: combination of Mediterranean low pressure -Sudan low pressure and interaction
with Azores and European anticyclone;
Type 3: interaction between Sudan low pressure and European high pressure tongue;
Type 4: Interaction among Tibet high pressure, Azores high pressure, and polar low
pressure;
Type 5: Thermal low pressures and Indian monsoon system.

کلیدواژه‌ها [English]

  • Atmospheric Pattern Classification
  • Low Pressure Dynamic
  • Principal component analysis
  • Siberian High Pressure