Journal of Climate Research

Journal of Climate Research

Comparison of correlation and mean methods in selecting Indicator days of sea level pressure patterns combined with heavy and pervasive rainfall in Iran

Document Type : Original Article

Authors
Hossein Asakereh, 1
saeideh ashrafi 2
1 Professor in Climatology, Department of Geography,University of Zanjan
2 Tabriz university
10.22034/jcr.2025.196182
Abstract
Spatial diversity and temporal variability of natural phenomena make their study time-consuming and difficult. On the other hand, there are mutual relationship between the phenomena and aspects and the position of their performance, that have spatial and temporal homogeneity. Based on these criteria, space (time) can be divided into independent units (Kavyani and Alijani, 2001: 344). therefore, classification is the systematic grouping of phenomena or events that have common characteristics or relationships within groups (Heydari, 1999: 8). Climatic phenomena are one of the important and most variety things in the nature of the world. There is no environmental-climatic phenomenon that is not caused by a specific pattern of air pressure distribution (Alijani, 2006: 201). Air pressure is an element that can be used to determine the characteristics of climatic elements, including temperature, precipitation, etc... Therefore, pressure systems are the main and independent factors that control characteristics of humidity, temperature and pressure with their temporal and spatial changes. Most of these pressure systems have a medium or synoptic scale (Boucher, 2006: 5). Therefore, in this study, sea level pressure was chosen as the climate index. Heavy rainfalls are one of the climate phenomena that can have many effects on the environment. This kind of rainfalls are most important natural reason of floods that cause irreparable damage to the natural environment and human societies. Therefore, the study of heavy rainfalls, especially in pervasive areas, is essential to understand the behavior of this type of rains. Identifying the behavior of this type of precipitation is an important step in water resources management. According to the amount of available data and for the ease of knowing the behavior of heavy rainfalls, we will need to use classification. Because despite the variety of patterns leading to heavy rainfall, there are similarities in these patterns.



Data and Method



In this research, heavy and pervasive rainfall in Iran has been studied by using the third version of Esfazari database. For this purpose, the values of the 90th, 95th and 99th percentiles of precipitation on rainy days were calculated for all cells of the map. This means that for each cell a specific threshold was obtained. Then, data above the 90th, 95th, and 99th percentiles were identified respectively as heavy, very heavy, and extremely heavy rain days. To determine the threshold for the definition of " pervasive rainfall " in this research, at first, anomaly of heavy rainfall area was calculated, and then the days with a positive anomaly was selected. Then the 75th percentile of the area of the selected days were obtained. The 75th percentile value obtained for the mentioned days is equal to 16% of the country's area and it was determined as the threshold of pervasive area. Result shows that 160 days with pervasive heavy rainfall (with precipitation equal or more than 90th percentile) occurred during the study period. Rainfall data of 160 days classified by using Binary cluster analysis. For this purpose, the Euclidean distance method and "Ward" linkage method were used. After identifying the number of heavy rainfall patterns, Indicator Days for each pattern identified by using correlation method (Lund) and the average area method. After identifying the Indicator days by both methods, by using sea level maps and the upper atmosphere levels and checking moisture convergence flux maps, Omega and statistical methods, the Indicator days obtained through both methods are compared.



Results and Discussion



As mentioned above, 160 days with pervasive heavy rainfall were identified and then classified by using binary cluster analysis method. Result show that the 160 studied days are placed in 10 groups (patterns). After determining the number of patterns, the indicator days for each pattern were identified by using correlation method and the average area method. Then, the indicator (obtained from both methods) days were compared by using sea level pressure maps and upper atmosphere levels and checking moisture convergence flux maps, Omega and statistical methods. Result show that average area method can choose indicator days better than correlation method. In the other hand indicator days chosen by correlation method most of the patterns are attributed to the Red sea low pressure and the role of other factors is small or ignored. For example, in this method, the role of the Indian Ocean in providing moisture for the heavy rains in the east and southeast of the country has been very small. But on the indicator days selected by the area average method, the pressure centers are arranged in such a way that a relative balance has been established in the role of factors that cause heavy rainfall.



Conclusion

The results of this research show that 160 days with heavy rainfall (with precipitation equal or more than 90th percentile) and in the area of more than 16% of the country (pervasive) occurred during the study period. The classification shows that 8 synoptic patterns cause heavy rainfalls. Also, Result show that average area method can choose indicator days better than correlation method.
Keywords
heavy rainfall day
pervasive
percentile threshold
Iran
1-    Ahmadi I, Alijani B (2014) Identification of synoptic patterns causing heavy rainfall in northern coast of Persian Gulf. Physical Geography Research Quartely 46(3): 4 – 5. doi: 10.22059/jphgr.2014.52132 (In Persian)
2-    Alexander LV, Zhang X, Peterson TC, Caesar J, Gleason B, Klein Tank AMG, Haylock M, Collins D, Trewin B, Rahimzdeh F, Tagipour A, Kumar Kolli R, Revadekar JV, Griffiths G, Vincent L, Stephenson DB, Burn J, Aguilar E, Brunet M, Taylor M, New M, Zhai P, Rusticucci M, Vazquez Aguirre JL. (2006). Global observed changes in daily climate extremes of temperature and precipitation. Journal of Geophysical Research – Atmospheres 111: D05109, DOI:10.1029/2005JD006290
3-    Alijani B (2006) Synoptic Climatology, Samt Press, Second Edition Tehran, Iran (In Persian)
4-    Alizadeh A (2009) Principles of applied hydrology. Imam Reza University press, Mashhad, Iran (In Persian)
5-    Almazroui. Mansour, Dambul. Ramzah, Nazrul Islam. Md and Jones. P.D (2015), Atmospheric circulation patterns in the Arab region and its relationships with Saudi Arabian surface climate: A preliminary assessment, Atmospheric Research 161–162,P 36–51
6-    Asakereh H (2011) Foundations of statistical climatology. Zanjan University Press, Zanjan, Iran (In Persian)
7-    Asakereh H (2012) Frequency distribution change of extreme precipitation in Zanjan City. Geography and Environment Planning 23:51-66 (In Persian)
8-    Asakereh, H, Khosh Raftar, R and Sotudeh, F (2012), Analysis the Relation of Synoptic Patterns with Daily Heavy Precipitation in Guilan Province During September (1976 – 2005), Physical Geography Research Quarterly, Vol 44, N 80, p 51 – 66 (In Persian)
9-    Asakereh, Hossein, Masoudian Seyed Abolfazl and Tarkarani, Fatemeh (2020), Long - term trend detection of annual precipitation over Iran in relation with changes in frequencies of daily extreme precipitation, Journal of geography and environmental hazard, Vol 9, N 36, https://doi: 10.22067/geoeh.2021.67028.0
10-    Asakereh, Hossein, Tarkarani, Fatemeh and Soltani, soghra (2013), On Tempo-Spatial Characters of Extreme Daily Precipitation of Northwest of Iran, Iran-Water Resources Research, Vol 8, No 3.
11-    Ashrafi, Saeideh (2013), Regionalization of very heavy rainfall cycles in Sistan & Balouchestan, Geographic Notion, Vol 7, No 13.
12-    Balyani, Saeed and Saligheh, Mohammad (2016), The Analysis and Extraction of Daily Heavy Rainfall Lead Atmospheric Patterns in Northern Portion of Persian Gulf (Helle and Mond Sub Basin), Journal of Spatial Analysis Environmental hazards, Vol 3, N 2, P 79 – 98 (In Persian)
13-    Barati, G., Bodagh Jamali, J., Maleki, N. (2012). Anticyclones and Heavy Rainfalls over Western Iran. Physical Geography Research Quarterly, 44(2), 85-98. doi: 10.22059/jphgr.2012.29208
14-    Boucher K, translated by bohlool Alijanin (2006), Global climate (volume 2) Subtropical region, Samt press (In Persian)
15-    Farifteh, J (1987), Climatic classification systems with emphasis on a case study in semi-arid and arid regions of Iran, Desert, No 20 (In Persian)
16-    Farshadfar E (2005), Multivariate Principles and Procedures of Statistical, taghe-bustan press, second edition (In Persian)
17-    Fatahi I, HejaziZadeh Z (2006) Temporal and Spatial Air Masses and its Application in Monitoring Dry and Wet Periods (Spells) in Southwest Basins of Iran. Geographical Research 21: N 81 (In Persian)
18-    Ghaedi, S., Movahedi, S., Masoodian, A., Rahimi, D. (2012). The Impact of Red Sea Trough on Iran Precipitation. Geography and Environmental Sustainability, 1(1), 63-78 (In Persian)
19-    Ghavi Del, Y (2010), Display and Describe Sinoptic climate by Usind Grads Soft Ware, Soha Danesh Press, First Edition (In Persian)
20-    HejaziZadeh, Z., & Jahfarpoor, Z., & Parvin, N. (2008). The Study and Recognition 500 hpa Level Synoptic Patterns Generating of Spread and Destroy Floods Over Urmia Lake Basin. Journal of Geographical Sciences, 7(10), 125-156 (In Persian)
21-    Heydari H (1999) Analysis of climatic elements in Iran for determining a classification pattern, PHD thesis, Tarbiyat Modarres University (In Persian)
22-    Kavyani M R; Alijani B (2001) The foundations of climatology. Samt Press, Tehran, Iran (In Persian)
23-    kiani, M., Lashkari, H., Ghaemi, H. (2020). Synoptic analysis of winter (DJF) extreme rainfall in western Iran. Researches in Earth Sciences, 11(3), 223-244. doi: 10.52547/esrj.11.3.223 (In Persian)
24-    Lashkari H; Khazaie M (2014) Synoptic analysis of heavy rainfalls in Sistan & Baluchestan. Sepehr 90: 70 – 79 (In Persian)
25-    Lund. Iver. A (1963), Map-Pattern Classification by Statistical Methods, Journal of Applied Meteorology, Vol 2, 56-65, DOI: https://doi.org/10.1175/1520-0450(1963)002<0056:MPCBSM>2.0.CO;2
26-    Masoodian S; Jafari Shendi F (2015) The Relationship between Synoptic Systems Influencing Heavy Rainfall in the Northern Low Rainfall Region. Geography and Planning 50: 305 – 331 (In Persian)
27-    Masoodian S; Karsaz S (2015) Synoptic analysis of thickness patterns at the time of  heavy and extensive precipitations of south Zagros area. Geography and Development 12 (37):15 – 27, doi: 10.22111/gdij.2015.1816 (In Persian)
28-    Masoodian SA; kavyani M R (2008) Climatology of Iran. Isfahan Univercity Press. Isfahan, Iran (In Persian)
29-    Masoodian SA; Asakereh H; Mohammadi B, Halabian A H (2012), Atmospheric Data Analysis and Display, Esfahan University press, First Edition (In Persian)
30-    Michailidou, C& P. Maheras & A. Arseni-Papadimititriou & F. Kolyva-Machera &C. Anagnostopoulou (2009), A study of weather types at Athens and Thessaloniki and their relationship to circulation types for the cold-wet period, part II: discriminant analysis, Theoretical and Applied Climatology, 97, 179–194
31-    Mohamadyariyan M, Tavousi T, khosravi M, Hamidiyanpour M. (2019), Zoning of Iranian Heavy Precipitation Regime, GeoRes 34 (2) :183-192
32-    Nouri H; Ghayoor H; Masoodian A; Azadi M (2013) Investigation on temperature in sea- air interaction of Caspian Sea for heavy and super heavy precipitation in the southern coasts. Geographical Research 27:199 – 223 (In Persian)
33-    Omidvar, K., Dehghan, H., Forodi, M. (2013). Synoptic Analysis of Floods in Bavanat Marvast River in Yazd. Geography and Territorial Spatial Arrangement, 3(8), 27-40. doi: 10.22111/gaij.2013.1285 (In Persian)
34-    Rahimi D; Khoshhal J; Alizadeh T (2011) The statistical –synoptic analysis of heavy showers in arid regions of Iran: Kerman Province. Journal of Geography and Regional Development 14: 51-69. doi: 10.22067/geography.v8i14.8996 (In Persian)
35-    Seyedan J and Mohammadi (1997), Climatic classification methods, Geographical Researches, No 45, 74 – 109 (In Persian)
36-    Zhang. Yang, Ding. Aijun, Mao. Huiting, Nie. Wei, Zhou. Derong, Liu. Lixia, Huang. Xin, Fu. Congbin (2016), Impact of synoptic weather patterns and inter-decadal climate variability on air quality in the North China Plain during 1980-2013, Atmospheric Environment 124, P 119-128
37-    Zhao. G.-J, Hörmann. G, Fohrer. N, Gao. J.-F, Zhai. J.-Q, Zhang. Z.-X (2009), Spatial and temporal characteristics of wet spells in the Yangtze River Basin from 1961 to 2003, Theor Appl Climatol 98:107–117, DOI 10.1007/s00704-008-0099-0
Journal of Climate Research
Volume 1403, Issue 59 - Serial Number 59
Vol. 15, No. 59,, Autumn 2024
Winter 2025
Pages 1-16