| چکیده انگلیسی مقاله |
IntroductionAtmospheric circulation patterns play the main role in the natural phenomenon occurring on theearth, especially in temperate regions. Some atmospheric circulation patterns cause wet periodsand others cause low water and dried periods. Thus, because the annual occurrence of droughtand wet events result from the general circulation of the atmosphere, recognizing atmosphericcirculation patterns are explained, to some extent, for the possibility of evaluating thesephenomena before occurance. Studies show that the floods and droughts phenomenan areinfluenced by atmospheric circulation patterns. Given the close relationship between thepatterns and climatic elements, we can also attribute the extreme climatic events, such as floodsand droughts, and dried and wet periods, to changes in atmospheric circulation patterns. In thisstudy, from average daily data balance of 500 and the sea level pressure over the period 1960 to2008 at two degrees intersection of the reconstructed data setshave been used. The selectedrange covers all systems affecting the area under study during the year. This range consists of408 cells from 20 to 60 degrees in north latitude and 10 to 70 degrees in east longitude. Totaldaily rainfall data from selected synoptic stations over the statistical period 1960-2008 wereused to assess the role of the patterns in rainfall. Many climate scientists dealing with variableswith different scale or large volumes of data employ reduction variable and data strategy byprincipal component analysis (PCA), (Gadyial, S. and R. N. Lyengar 1980, Kalkstein. S. et al.1998).∗E-mail: F_babaee@pnu.ac.ir Tel: +98 9123180576Physical Geography Research Quarterly, 46 (1), Spring 2014 5MethodologyFactor analysis is a statistical technique that establishs a especial relationship among a largenumber of variables that are seemingly unrelated. It is under a hypothetical model and gathersall the variables in the similar groups. This method retains significant and main components inthe same groups and reduce the variables. One of the results of factor analysis is to reduce datadimension. Computational steps of the main component analysis is as follows:a) The data and variables Selection. b) The second stage of a data matrix p × n formationwhere n is the number of days and p is the number of variables. In the third stage since theselected meteorological variables of the unit are different (For example, C, hPa, meters persecond, and so on), a correlation matrix was used as input for the main component analysis.Data correlation matrix are calculated according to the following formula. The fourth step isused to determine the number of factors by Catel test. Loadings matrix was calculated in thefifth stage. Loadings show the relationship between the factors and the primary variables.The relationship between atmospheric circulation patterns and rainfallTo evaluate the relationship between atmospheric circulation patterns and rainfall, the followingindex is applied. This index defines the conditional probability of rainfall occurnece and rainfallintensity in a circulation pattern. The index defines a measure of the relative share of the patternrainfall in total. Where ni is the number of days with i patterns and Ri is the total rainfall duringthat days and n is the number of days in the period of the study. If PI< 1.Or even much smaller than the unit, the weather or type pattern i does not greatly affect thearea rainfall. Thus, an increase in the frequency of occurrence of such a pattern, reduces rainfalland subsequently, causes drought in a region. If the PI in the statistical method is greater thanthe unit, then chance of rain (probability of precipitation) also increase and wet periods will beprevailed. For example, precipitation takes place when weather is wet and there is an acsendingfactor, these conditions are provided by atmospheric circulation patterns.Results and DiscussionIn this study, using PCA and clustering, eighteen circulation patterns according to the sea levelpressure and 500 hPa level atmospheric condition have been identified over the study area. Theresults of this study show that there are significant differences in the arrangement of patterns,the weather type frequency and the way they move towards the study region. The PI index is aappropriate criterion to evaluate the Conditional probability of rainfall and rainfall intensity. Ifthe PI index calculated for a wheather type much smaller than unit, wheather type does not playa role in precipitation of that station or region. Therefore, an increase in the frequency ofoccurrence of such a pattern in a period reduces rainfall and makes the drought events in thatregion.ConclusionDue to the PI index and the annual frequency distribution of atmospheric circulation patterns,the results can be summarized as follows. Atmospheric circulation patterns of CP1, CP4, CP5,6 Physical Geography Research Quarterly, 46 (1), Spring 2014CP12, and CP15 are part of the patterns leading to heavy and pervasive precipitation.Atmospheric circulation patterns of CP7, CP13, CP16, CP17, and CP18 are part of the patternsleading to moderate precipitation. Atmospheric circulation patterns of CP2, CP8, CP9, CP10,and CP11 are part of the patterns leading to drought, Atmospheric circulation patterns of CP3,CP6, and CP14 are part of the patterns leading to drought. In terms of the annual frequencydistribution, atmospheric circulation patterns of CP3, CP5, CP13, and CP15 are active in allseasons of the year, atmospheric circulation patterns of CP2, CP6, and CP10 are active insummer, atmospheric circulation patterns of CP1, CP8, CP9, CP11, CP12, CP14, CP16, CP17,and CP18 in winter, spring and fall and atmospheric circulation patterns of CP7 is active in thespring and fall. |