TY - JOUR ID - 52132 TI - Identification of Synoptic Patterns Causing Heavy Rainfall in Northern Coast of Persian Gulf JO - Physical Geography Research JA - JPHGR LA - en SN - 2008-630X AU - Ahmadi, Ismael AU - Alijani, Bohloul AD - Ph.D. Candidate in Climatology, Dep. of Geography Sciences, Kharazmi University, Tehran, Iran AD - Prof. in Climatology and Director of Center of Excellence for Spatial Analysis of Environmental Hazards, Dep. of Geography Sciences, Kharazmi University, Tehran, Iran Y1 - 2014 PY - 2014 VL - 46 IS - 3 SP - 275 EP - 296 KW - heavy rainfall KW - K-means KW - Persian Gulf KW - Self-organizing Map KW - synoptic pattern KW - U*-matrix DO - 10.22059/jphgr.2014.52132 N2 - IntroductionSometimes, the showers of the northern coast of Persian Gulf are very heavy and disastrous andhazardous. They cause heavy damages to the people and the infrastructures of the region.Therefore the economic development of the area is highly dependent upon the identification ofthe cause and management of these hazardous phenomena. The main factor controlling thesurface climate is the pressure patterns of the atmosphere. Therefore, the main objective of thisstudy is to identify the synoptic patterns of these showers. Thus, we can predict their occurrenceand mitigate their damages. The successful achievement is dependent on two major factors:(a) the methodology of pattern recognition and (b) identification of actual patterns. Most of themodels of pattern analysis are linear while the atmospheric processes are non-linear in nature.Any methodology that neglects the nonlinear nature of atmospheric phenomena would result ininadequate classification of atmospheric circulation. For this reason, this research has used thenonlinear models of classification algorithms to identify the pressure patterns of the heavy rainsof the area.MethodologyThe study was based on the hypothesis that the daily atmospheric circulation can be explainedby the geo-potential height of 500 hPa level, precipitable water, and the velocity of vertical􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯∗E-mail: ahmadi.ism@gmail.com Tel: +98 9129376680Physical Geography Research Quarterly, 46 (3), Fall 2014 5patterns and heavy rainfall, the data have been collected through 15340 days (1966-2007) forthese three variables of 289 grid-points, with a resolution of 2.5 degrees, from NCEP database.Daily rainfall data for the same period have also been gathered for Bandar-e-Abbas, Bandar-e-Lengeh, Boushehr, and Abadan stations from Meteorological Organization of Iran. First, thedaily circulations as micro-patterns have been classified using self-organizing map (SOM)algorithm, a type of unsupervised neural network. This algorithm begins to calculate theEuclidian Distance between an input vector and all of the weight vectors to find the 'winner' unit(BMU) with the weight vector closest to the input vector. The calculation continues to update allthe weight vectors, especially those within neighbouring radius .The iterative calculationproceeds towards the projection of similar data samples in the high dimensional, complex inputdata space to an identical unit area in the map. As a result, the neighbouring units in the map aresimilar to each other while distant units are dissimilar. Then, the U*-matrix, as a suitablemethod for two-dimensional visualization of the trained SOM that enabled us to recognize thedegree of the similarity among adjacent units in the two-dimensional map, was employed toidentify boundaries among clusters and to extract the actual number of meso-patterns. Finally,K-means method was utilized to cluster these meso- patterns into distinguished macro patterns.Results and DiscussionThe results revealed that SOM, by classifying the micro-patterns into 289 meso-patterns, coulddiscriminate the days of warm and cold periods with an accuracy of more than 99 percent.These patterns were classified into 11 macro patterns through the U*-matrix and K-meansmodels. Through displaying the number of heavy rainfall events in each station on each unit ofSOM, it was specified that four macro-patterns explained up to 83 % of heavy rainfall events ofthe region. These patterns are named as follows: Pattern No. 4 as Syria trough becomes deeper,Siberian high pressure moves towards west, and the moisture of Arabian and Oman Seas moveto PG. Similarly, the identification of pattern 6 is possible by Sudan low, subtropical jet streamvelocity increase, and its base decreases. Pattern 7 is identified by cut-off low system, very lowpressure, and closed low up to upper troposphere. Pattern 9 is specified by two characteristics:(a): the simultaneous presence of warm and cold season components of atmosphere during theseasonal change, and (b) dense isobars over PG.ConclusionOn the basis of the results, we concluded that the combination of SOM classification method,U*- matrix and K-means clustering methods can be employed as an appropriate instrument toclassify nonlinear atmospheric variables, in one hand, and to resolve the problem of extractingthe actual synoptic patterns, on the other. Of the four synoptic patterns of heavy rainfall, cut-offlow and seasonal transition patterns should be taken into account more seriously because of thepersistence and startling nature of their heavy rainfall as well as the vulnerability of society forthe probable damage. UR - https://jphgr.ut.ac.ir/article_52132.html L1 - https://jphgr.ut.ac.ir/article_52132_bec45b978afb3b126e73b3eb33fba946.pdf ER -