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Harmonic Analysis of the Spatiotemporal Pattern of Thunderstorms in Iran (1961–2010)

Harmonic Analysis of the Spatiotemporal Pattern of Thunderstorms in Iran (1961–2010) Hindawi Advances in Meteorology Volume 2019, Article ID 1612503, 14 pages https://doi.org/10.1155/2019/1612503 Research Article Harmonic Analysis of the Spatiotemporal Pattern of Thunderstorms in Iran (1961–2010) 1 2 3 Ali Akbar Sabziparvar , Seyed Hossein Mir Mousavi, Mostafa Karampour, 4 5 6,7 8 Mehdi Doostkamian, Esmaeil Haghighi , Iman Rousta , Haraldur Olafsson, 9 10 11 12 Md Omar Sarif , Rajan Dev Gupta, Md Moniruzzaman, Khairul Hasan, and Ali Ghasemi Department of Agriculture, Bu-Ali Sina University, Hamedan, Iran Department of Geography, University of Zanjan, Zanjan, Iran Department of Geography, Lorestan University, Khorramabad, Lorestan 6813833946, Iran Department of Geography, University of Zanjan, Zanjan 3879145371, Iran Department of Physical Geography, University of Tabriz, Tabriz, Iran Department of Geography, Yazd University, Yazd 8915818411, Iran Institute for Atmospheric Sciences, University of Iceland and Icelandic Meteorological Office (IMO), Bustadavegur 7, IS-108 Reykjavik, Iceland Department of Physics, University of Iceland, Institute for Atmospheric Sciences and Icelandic Meteorological Office (IMO), Bustadavegur 7, IS-108 Reykjavik, Iceland Geographic Information System (GIS) Cell, Motilal Nehru National Institute of Technology Allahabad, Prayagraj-211004, India Civil Engineering Department, and Member of GIS Cell, Motilal Nehru National Institute of Technology Allahabad, Prayagraj-211004, India Center for Space Science and Technology in Asia and the Pacific (CSSTEAP), Dehradun-248001, India Department of Civil and Environmental Engineering, Shahjalal University of Science & Technology, Sylhet-3114, Bangladesh, India Department of Physical Geography, University of Tabriz, Tabriz, Iran Correspondence should be addressed to Esmaeil Haghighi; s.haghighi1985@gmail.com and Iman Rousta; irousta@yazd.ac.ir Received 30 August 2018; Revised 9 December 2018; Accepted 23 December 2018; Published 11 February 2019 Academic Editor: Helena A. Flocas Copyright © 2019 Ali Akbar Sabziparvar et al. +is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. +e current study aimed at investigating cycles and the spatial autocorrelation pattern of anomalies of thunderstorms in Iran during different periods from 1961 to 2010. In this analysis, 50-year periods (1961–2010) of thunderstorm codes have been collected from 283 synoptic stations of Meteorological Organization of Iran. +e study period has been divided into five different decades (1961–1970, 1971–1980, 1981–1990, 1991–2000, and 2001–2010). Spectral analysis and Moran’s I were used to analyze cycles and the spatial autocorrelation pattern, respectively. Furthermore, in order to conduct the calculations, programming facilities of MATLAB have been explored. Finally, Surfer and GIS were employed to come up with the graphical depiction of the maps. +e results showed that the maximum of positive anomalies mainly occurred in the northwestern and western parts of Iran due to their special topography, during all the five studied periods. On the other hand, the minimum of negative anomalies took place in central regions of the country because of lack of appropriate conditions (e.g., enough humidity). Moran’s I spatial analysis further confirmed these findings as Moran’s I depicts the positive and negative spatial autocorrelation patterns in line with negative and positive anomalies, respectively. However, in recent decades, this pattern has experienced a declining trend, es- pecially in southern areas of Iran. +e results of harmonic analysis indicated that mainly short-term and midterm cycles dominated Iran’s thunderstorms. 2 Advances in Meteorology to find the reasons behind the occurrence of thunderstorms, 1. Introduction and his findings showed that thunderstorms have a biannual +understorm is a climatic phenomenon that can have cycle which has a weak correlation with the solar cycle. destructive effects on agriculture, civil facilities, structures, Furthermore, he discovered that the maximum number of and economy [1]. In addition, thunderstorms are usually thunderstorms happened in June [5]. Enno et al. used the accompanied by other climatic phenomena (like hail, heavy Mann–Kendall test in order to analyze the frequency trend rainfall, snow, and lightening), which can also claim the loss of thunderstorms in Baltic countries within the period of numerous human lives [2, 3]. Although natural disaster 1950–2004, and his results indicated a 24% decline in the cannot be prevented, its deleterious consequences can be number of days in which thunderstorms happened. He predicted and minimized through studying thunderstorms recorded a rate of 0.9 day decline for each decade [17]. +e over long-term periods. By so doing, the degree of the similar research projects conducted by others (Yu and Lee, vulnerability of structures and civil facilities can be de- Abhilash et al., Lolis, and Mastrangelo et al.) are some other termined, leading to the construction of stronger structures examples in this regard [18–21]. in future. Furthermore, studying thunderstorms will reduce Iran is located on the way of different air masses, has human causalities to a great extent [4, 5]. Many factors such different physiographic units, and encompasses various as topography, land cover, and atmospheric processes are geographical latitudes [22–27]. +erefore, it has favorable effective on the occurrence of thunderstorms [6]. +un- conditions for the occurrence of thunderstorms. In Iran, the derstorms may be the result of severe gradient between two northwest and southwest of the Zagros mountain range systems [7, 8]. A number of studies have indicated that experience two opposite conditions; that is, during the height plays a significant role in the onset and evolution of transition from cold to warm season, the number of storms [9]. However, several studies have been conducted in lightning in the northwest of Iran is greater than that of the this field. For example, Davis investigated daily variations of southwest. On the other hand, during the transition from thunderstorms in Heathrow airport, London. +e results warm to cold season, lightning in southwest outnumbers the indicated that the majority of thunderstorms occurred in the one in northwest [28]. +e majority of precipitation in Iran afternoons. Also, some of them happened at midnights [10]. is influenced by the Mediterranean pattern [26, 27, 29–35]. However, Callado and Pascual Berghaenel used different When this pattern is combined with the Sudanese low models to study the Mediterranean thunderstorms, and they pressure and faces the topography of southwest of Iran, argued that the high frequency of thunderstorms in Cata- forced convective climb and instability occur. +us, the lonia at the end of the spring and in summer can be at- humidity of Arabian Sea moves toward southwest and tributed to local mountains and maritime influences [11]. conditions become favorable for thunderstorms [8, 33, 36]. Davolio et al. simulated the intense convective precipitation At the same time, local instabilities during warm seasons, th th event of September 8 and 9 , 2002, in the southeast of especially in higher areas, intensify rain showers. However, France, and they believed that the existence of a midsize one cannot ignore the role of foreign cold front, which enters the country during the cold season, in creating lightning [2]. convective system before the approaching of cold front was responsible for this event [12]. Trentmann et al. concentrated On the other hand, spatial changes of Azores High pressure th on variations in convective precipitations of July 12 , 2006, have different influences on Iran’s climate. More precisely, in Central Europe and found that the maximum amount of this system moves toward north and east, withdraws from available convective energy for these precipitations was the south because the polar trough low pressure advances in registered early afternoon, and they also concluded that this area, and interacts with Gang low pressure. +ese topography played a significant role in the occurrence of phenomena cause instability and showers. Furthermore, the these precipitations [13]. Kunz et al. studied the frequency two cells of Sudanese low pressure and Mediterranean are trend of thunderstorm during 1974–2003 in southwest combined over Kuwait and south of Iraq, with their troughs Germany, and they showed that the annual frequency of being extended to the northwest of Iran. Also, a low pressure days with thunder and lightning had remained almost intact. cell is formed over the strait of Hormoz and the north of Arabian Peninsula. +ese phenomena cause warm weather However, the number of days with hail and its consequent damages had risen significantly [2]. Lin-Lin et al. focused on and humidity of the Sea of Oman and the Persian Gulf to changes in thunderstorms within different decades and their move over the region, hence making the conditions more annual distribution, and their results demonstrated that favorable for instability and lightning [8]. Considering all thunderstorm distribution varied in different seasons. +at these into account, various instability indices, such as is, while in May, most of the thunderstorms happened in the SWEAT, TTI SOI, KI, SI, CIN, and CAPE, and the pre- north and in September, the majority of them occurred in cipitation water (PW) index can provide good evaluations of the south [14]. Mohee and Miller used radar data and earth thunderstorms. Among these indices, the Showalter index surface data in North Dakota in order to come up with the (SI) has yielded better results. climatic features of thunderstorms, and their results sup- Accompanying thunderstorm (with lightning, tornado, ported the idea that thunderstorms mainly happened in the hail, winds, heavy precipitations and hazardous atmospheric afternoon and early morning in June [15]. Loginov et al. phenomena like turbulence, freezing, and wind sheering) studied the formation and variations of some thunderstorms makes considerable irrecoverable damages to natural and in Belarus, and their results revealed that the thunderstorms human environments. However, most of the studies in Iran had a lot of temporal and spatial variation [16]. Mic intended have used different research methodologies to investigate Advances in Meteorology 3 thunderstorms in the northwest and southwest of the 2 2πq n a � 􏽘 x cos􏼒 t􏼓 q � 1, 2, . . . , , (3) country. Until now, no comprehensive study has been i t n n 2 t�1 conducted to include all areas of the country. As a result, the present study aimed at investigating changes in the spatial 2 2πq pattern of Iran’s thunderstorms within the last five decades b � 􏽘 x sin􏼒 t􏼓 q � 1, 2, . . . , n, (4) i t n n (1961–2010). t�1 where q is the number of harmonics. For even series, there 2. Methodology will be q � n/2 harmonics, whereas for odd series, there will be q � (n− 1)/2 harmonics. In order to analyze spatial autocorrelation cycles and patterns +e variance of each frequency (wave) was calculated of anomalies of Iran’s thunderstorms, thunderstorm codes (91 through the following formula: to 99, 17, and 29) of a 50-year period were collected from 283 2 2 I f � a + b . synoptic stations of Iran’s Meteorological Organization 􏼁 􏼐 􏼑 (5) i i i (IRIMO). +ese data were prepared and validated by the IRIMO and have not been missing values during the studied In order to investigate dominant patterns of Iran’s statistical period. thunderstorms within decades, geostatistical methods of (Figure 1 and Table 1). Moreover, in order to come up spatial autocorrelation (Moran’s I) were used. +is pro- with a periodical analysis of thunderstorms, the study cedure helps discover whether the distribution pattern of the period was divided into five time intervals (1961–1970, studied phenomenon follows a cluster or random pattern. In 1971–1980, 1981–1990, 1991–2000, and 2001–2010). fact, in this procedure, Moran’s I is calculated, and Z score When it comes to climate-related data, greater record and significance level are used to evaluate the calculated lengths are more valid and reliable. +erefore, the re- index. +is type of analysis shows the regions in which the searchers selected those stations which have the greatest studied phenomenon has been clustered and has followed a record length (Figure 1). significantly different pattern compared to the same phe- First, with the aim of gaining a general understanding of nomenon in the surrounding areas. As previously men- thunderstorms, descriptive data were presented for each tioned, the preassumption of this type of analysis is that the period. +en, in order to gain a detailed understanding, studied phenomena are weighed; hence, the clusters which anomalies and center mean of thunderstorms for each pe- have similar size can be easily identified. On the other hand, riod were analyzed. Average centers, which are the average the patterns that do not form a cluster can be recognized too. gravity of spatial distribution, are defined by equations (1) Moran’s I is calculated through equation (6) or equation (7). and (2): Z score is calculated by equation (8). 􏽐 P X x − X i�1 i i X � , (1) I � 􏽘 w x − X􏼁 , c n (6) i.j i 􏽐 P i�1 i j�1 j≠i 􏽐 P Y where x is the amount of phenomenon on the i th cell, X i�1 i i Y � , (2) c n is the mean of that phenomenon, and w is the spatial 􏽐 P i.j i�1 i weight between the two phenomena (i and j). Also, where P is frequency of thunderstorms and X and Y are i i i 􏽐 w j�1 j≠i i.j 2 latitude and longitude, respectively. (7) S � − X , n− 1 In order to have a more accurate analysis of thun- derstorm changes within each decade, spectral analysis where n is the number of all phenomena. Standard z I is was utilized to analyze thunderstorm cycles for each calculated through the following formula: period. +e distribution of variance along all wavelengths I − E􏼂I 􏼃 i i of the time series is known as spectral analysis. In fact, the 􏽱���� � z � , harmonic analysis technique entails analyzing the vari- V􏼂I 􏼃 (8) ance of a time series. In spectral analysis, time series are first converted to frequency functions (in the form of 2 j�1,j≠1 And : V I � E I − E I E I � − . periodic functions with amplitude and frequency). In such 􏼂 􏼃 􏽨 􏽩 􏼂 􏼃 􏼂 􏼃 i i i i n− 1 functions, frequency indicates the time scale (cycles within a time unit) and amplitude represents variance in After conducting thunderstorms’ cycle analysis and this time scale. +erefore, in this technique, each of the spatial pattern analysis, in order to determine the number of waves is extracted, and their contribution to the total regions, cluster analysis is used. In fact, in this study, variance is assessed. +en, after determining the variance, “Ward’s Hierarchical Clustering” or the “method of mini- each of the waves is studied to see if they are statistically mum variance” analysis on average of thunderstorm oc- significant. In order to convert the time series to fre- currence was used for zoning and clustering Iran’s quency and calculate the harmonics, first, two parameters thunderstorms. +e method was used to group locations and should be calculated through equation (5) by equation (3) their distributions based on their similarity. Ward’s method and equation (4) [37]: looks at cluster analysis as a variance problem and computes 4 Advances in Meteorology W E 0 115 230 460 (kilometers) 46 48 50 52 54 56 58 60 62 Longitude Figure 1: Distribution pattern of the studied stations. Table 1: understorm’s code and its description (source: WMO However, since variance is big, this period has the highest code 4677 ). coeŽcient of variation. On the contrary, the lowest mean (6.86 days) belongs to the second period (1971–1980). Code Description Considering the periods and years, there are great dif- understorm, but no precipitation at the time of ferences between mean, mode, and median. is shows observation that thunderstorms followed dissimilar patterns in dif- understorm (with or without precipitation) during ferent times, hence not forming a normal distribution. e the preceding hour but not at the time of observation understorm, slight or moderate, without hail, but large di­erence of the quartile range further con‹rms this with rain and/or snow at the time of observation claim. CoeŽcient of skewness is positive for all periods, a understorm, slight or moderate, with hail at time of phenomenon that shows the inclination of thunderstorms observation. toward smaller values. is inclination has reached its understorm, heavy, without hail, but with rain peak during the ‹rst period (1961–1970). Kurtosis is used and/or snow at time of observation to describe the degree of sharpness (or –atness) of the understorm combined with dust/sandstorm at curve. When the kurtosis coeŽcient is positive, the fre- time of observation quency of extreme values above the mean in the data is 99 understorm, heavy with hail at time of observation greater than the extreme values less than the mean. If kurtosis is close to zero, the curve will be sharp. As ob- served, kurtosis value is positive for all periods. Studying Euclidean distances to evaluate dissimilarity between the the 25, 50, and 75 percentile rank shows that the ‹fth clusters. Ward’s algorithm, when implemented on a dataset, period (2001–2010) experienced a greater value compared establishes groups by minimizing the dissimilarity or the to other periods. total sum of squares. is algorithm calculates several On the other hand, the maximum number of thun- clusters at a level when intergroup similarity is maximized derstorms occurred in the same period (2001–2010), which while intragroup similarity is minimized [38–42]. indicates the wide range of di­erences in thunderstorms. It seems that the maximum number of thunderstorms has not randomly happened in this period; otherwise, it would not 3. Results in–uence the center mean of this period (this period had the e salient statistical features of thunderstorm of Iran for lowest mean). It can be concluded that, except the ‹fth di­erent periods (1961–1970, 1971–1980, 1981–1990, period (2001–2010) in which a considerable increase can be 1991–2000, and 2001–2010) have been extracted (Table 2). observed in the number of thunderstorms, the dominant Accordingly, the greatest spatial mean (15.36 days) for pattern of thunderstorms has experienced a rather stable thunderstorms happened in the ‹fth period (2001–2010). mechanism in all the other periods. Latitude Advances in Meteorology 5 Table 2: Spatial features of the frequency of Iran’s thunderstorms for different periods. Index 1961–1970 1971–1980 1981–1990 1991–2000 2001–2010 1961–2010 Average 7.7 6.8 10.5 13.6 15.3 10.9 Median 6.7 6.1 9.1 12.5 13.2 9.7 Mode 0.6 0.5 0.9 1.5 4.2 2.8 Variance 24.5 15.1 37.9 37.8 65 29 Standard 4.9 3.8 6.1 6.1 8 5.3 Coefficient 63.7 56.7 58.1 44.9 52.4 49.2 Range 31.1 20.1 30.3 32.5 34.9 28.3 Skewness 2.3 0.77 1 1 1 1.2 Kurtosis 9.9 2.8 3.3 3.7 3.3 4.3 Maximum 31.7 20.7 31.2 34.1 39.2 31.2 Minimum 0.6 0.5 0.9 1.5 4.2 2.8 Q1 4.5 3.6 5.8 9.5 9 7.1 Q2 6.7 6.1 9.1 12.5 13.2 9.7 Q3 8.9 9.5 13.2 16.2 19.9 12.6 3.1. Analyzing Anomalies and the Spatial Autocorrelation negative anomaly because of low humidity and lack of Pattern of ?understorms. In order to gain a more accurate convective climb. However, in the second period (1971– understanding of the changing pattern of thunderstorm 1980), positive anomalies significantly increased around 8% within each decade, mean, anomaly (the average of each year in comparison with the previous period, i.e., (1961–1970) in different periods relative to the average annual value of the (Table 2). As a result, the coastal areas of the Caspian Sea and whole of the period), and center of gravity (center mean) the Persian Gulf experienced positive anomaly during this period, a condition that can be explained in the light of high were drawn for each period (Figure 2). +e center of gravity focuses on the occurrence of climate phenomena in the humidity in these regions. In the same period (1971–1980), studied area. For example, if the focus of the points is in the the spatial coefficient of variation of thunderstorms in the northwest of Iran, this indicates that the spatial center of northwest of Iran had no significant change compared to the gravity of the storms is in the northwestern part of Iran. In first period (Figure 2). It, however, experienced up to 20% this procedure, for each period, thunder deviation from the increase in other areas, especially central parts of Iran. High normal distribution was calculated through the algebra map. coefficient of variation in these regions indicates that Some anomalies on earth are under the influence of thunderstorms had a lot of oscillations in these areas. On the random climate phenomena, while others are the conse- other hand, they become more or less stable toward the northwest of the country. quence of a particular pattern [43, 44]. For example, positive anomalies of middle latitudes are the result of ocean currents In all periods, the northeast areas of the country expe- rienced negative anomaly (Figure 2). In other words, those and El Nino-Southern Oscillation (ENSO) [45]. Widespread cold anomalies may be a symptom of a severe winter. Some areas had a fewer thunderstorms than the other areas. Some scientists use anomalies to identify normal and abnormal scientists believe that thunderstorms in this region occur natural disasters. For instance, some scientists claim that because of its topography, high altitude above sea level, and environmental problems, such as flood, storm, and drought, the systems that enter the region from north and south [8]. are caused by climatic oscillations, especially exacerbated However, in the third period (1981–1990), the intensity of temperature anomalies on earth [46–51]. positive anomalies reduced; hence, almost 75% of the Analyzing anomalies of various periods shows that most country’s area experienced negative anomaly (Table 3). But of the positive anomalies belong to the northwest and the amplitude of these anomalies oscillated between−14 and 16. +us, moving from the four corners of Iran toward southwest of the country (Figure 2). Alijani has demon- strated the occurrence of this maximum in his own study central parts, one can observe that the intensity of thun- derstorms declines, but their distribution increases. +is [33]. +ese positive anomalies may be the result of local conditions, especially topography [3, 52, 53], and/or dy- indicates that humidity plays a very important role in namic and synoptic factors [54]. For example, some re- creating thunderstorms. As mentioned earlier, the second searchers attribute this maximum anomaly to anticyclone maximum intensity of thunderstorms in all the periods is ridges over Russia that extend to the northwest of Iran and observed in the south and southwest of the country. Some increases thermal gradient in the region. In addition, low researchers believe that these thunderstorms are caused by pressure cells over the strait of Hormoz and the north of combined patterns. More precisely, the stretch of a ridge on Arabian Peninsula, heat transfer, and humidity of the Sea of the northwest of Africa to Scandinavia creates cold air on the Oman provide necessary conditions for instability and Mediterranean and deepens its trough. +is phenomenon strengthens the low pressure center of the eastern Medi- lightning in the region [8]. One of the necessary conditions for the occurrence of thunderstorms is the availability of terranean. As a result, the increase of thermal gradient over the north of Africa and Red Sea strengthens the Sudanese humidity [55]. +us, it is observed that despite their high coefficient of variation, the central parts of Iran have system; hence, this system moves toward the north and is 6 Advances in Meteorology –3 –4 –8 –10 4120000 4420000 4720000 5020000 5320000 5620000 5920000 4120000 4420000 4720000 5020000 5320000 5620000 5920000 0 500000 1000000 0 500000 1000000 (a) (b) –2 –4 –8 –10 –16 –14 4120000 4420000 4720000 5020000 5320000 5620000 5920000 4120000 4420000 4720000 5020000 5320000 5620000 5920000 0 500000 1000000 0 500000 1000000 (c) (d) 20 19 8 9 –1 –4 –6 –10 –11 –16 4120000 4420000 4720000 5020000 5320000 5620000 5920000 4120000 4420000 4720000 5020000 5320000 5620000 5920000 0 500000 1000000 0 500000 1000000 (e) (f) Figure 2: Spatial distribution of mean (contour), center of gravity (+), and anomalies (shaded) of Iran’s thunderstorms in different periods. (a) 1961–1970, (b) 1971–1980, (c) 1981–1990, (d) 1991–2000, (e) 2001–2010, and (f) 1960–2010. Latitude Latitude Latitude 30200000 30500000 30800000 31100000 31400000 31700000 30200000 30500000 30800000 31100000 31400000 31700000 30200000 30500000 30800000 31100000 31400000 31700000 Latitude Latitude Latitude 30200000 30500000 30800000 31100000 31400000 31700000 30200000 30500000 30800000 31100000 31400000 31700000 30200000 30500000 30800000 31100000 31400000 31700000 Advances in Meteorology 7 Table 3: +e percentage of country’s areas (studied stations) influenced by positive and negative anomalies of thunderstorms in different periods. Index 1961–1970 1971–1980 1981–1990 1991–2000 2001–2010 1961–2010 Negative 77.2 70 75.1 73.4 71.5 76.7 Positive 22.8 30 24.98 26.6 28.5 23.3 combined with the Mediterranean low pressure, thus is set at 0.05. +e results of Moran’s I analysis are presented causing instability and thunderstorms over Iran [8, 36]. in Figure 3. Studying changes in anomaly patterns on both peri- Some other researchers believe that these thunderstorms happen because of the front nature accompanying the ad- odical and annual scales show that according to Moran’s model, the maximum intensity of anomalies forms a vection of warm air in lower parts of the atmosphere, which happens in cyclone centers of 500 HPa [56]. +erefore, in the positive spatial autocorrelation. In other words, it is sur- third, fourth, and fifth periods (1981–1990, 1991–2000, and rounded by high value phenomena. On the other hand, the 2001–2010), anomalies have had more or less similar con- negative spatial autocorrelation pattern matches the ditions (Table 3). However, in the fourth period, spatial minimum of negative anomalies. However, except the first coefficient of variation of thunderstorms experienced fewer period (1961–1970) in which this pattern has been in- changes. For example, compared to the third period significant, the pattern experienced almost similar condi- (1981–1990), the thunderstorm coefficient of variation in the tions in other periods. For instance, in the first period, only southwest of the country experienced 40% decline in the 9.9% of the country’s area (mainly the northwest of Iran and southwest of the Caspian Sea) had positive spatial fourth period (1991–2000) (Figure 2). +ese changes had less oscillation in the south, southwest, and northwest of the autocorrelation (Table 4). At the same time, during this period, less than 3% of the country’s area formed low country (compared to other regions) during all the five periods. +is can be explained in the light of the appropriate clustering pattern (Table 4). +us, in none of the periods, conditions of these regions for the occurrence of thunder- HL (i.e., high values surrounded by low values) and LH storms. +e center of gravity was drawn for thunderstorms (i.e., low values surrounded by high values) patterns can be in order to identify their spatial behavior and regularity of observed. Since the second period (1971–1980), the area occurrence (Figure 2). +ese centers are highlighted with dominated by positive and negative spatial autocorrelation gray spots. As indicated, the direction and inclination of increased, hence experiencing little oscillation in other thunderstorms’ centers of gravity has a direct relationship periods. +e most dominant positive spatial autocorrela- with positive anomalies on the scale of both periods and tion pattern (around 20%) can be observed during the second period (1971–1980) (Table 4). Since then, high years. +at is, since in all the periods the maximum intensity of anomalies is located in the northwest and west of the clustering pattern experienced a significantly downward trend, which moves from the north to the south of Iran. In country, the centers of gravity are located on the northwest of Iran (Figure 2). +e density of these centers on a single contrast, negative spatial autocorrelation experienced some location during all periods shows that they follow a regular oscillations Figure 3. +e same findings have been obtained pattern. +e density and regularity of these centers are so by Araghi et al., hence verifying the results of this study important that even a small change in their location rep- [57]. In general, thunderstorms do not follow any partic- resents significant changes in thunderstorms. ular pattern in the largest area of the country. In order to display the dominant pattern of spatial distribution of Iran’s thunderstorms during various periods, 3.2. Analyzing Oscillations and Cycles of ?understorms. Moran’s I was employed (Figure 3). +e results of this data In order to gain a more accurate understanding of thun- analysis procedure indicate that whether the phenomena are derstorm changes within each decade, spectral analysis was randomly distributed or are clustered around a single point. used to assess cycles that dominate each period. In spectral If the value of Moran’s I is positive, it shows that the studied analysis, changes in time series are divided into parts that phenomenon is surrounded by similar phenomena and it is part of that cluster. On the other hand, if the value of have frequencies. +is procedure of data analysis is used to extract and analyze explicit and implicit oscillations with Moran’s I is negative, it means that the studied phenomenon is surrounded by dissimilar phenomena, which do not form various wavelengths. Figure 4 shows the spatial distribution of variances and cycles of thunderstorms. In this figure, a cluster. +is index is calculated based on a standardized score and interpreted in the light of the significance level. cycles are identified by colorful spectra and variances are shown in the form of contours. In this procedure, HH represents clusters with high It is observed that in all the periods, mainly short cycles values (i.e., positive spatial autocorrelation), LL shows of 2 to 4 years dominate Iran’s thunderstorms (in 95 percent clusters with low values (i.e., negative spatial autocorrela- level of significant). For example, in the first period tion), HL indicates lack of any cluster (i.e., a high value is surrounded by lower values), and LH shows that a low value (1961–1970), almost 75% of the country’s area had thun- derstorms with 2-year to 4-year cycles (Table 5). Most of the is surrounded by high values. For this type of analysis, the p-value 8 Advances in Meteorology HL N HH HL HH W E W E LL LH LL LH 0 100000 200000 300000 0 100000 200000 300000 (a) (b) HL HH HL HH W E W E LL LH LL LH 0 100000 200000 300000 0 100000 200000 300000 (c) (d) HL N HH HL HH W E W E LL LH LL LH 0 100000 200000 300000 0 100000 200000 300000 (e) (f) Figure 3: Spatial distribution of autocorrelation pattern of Iran’s thunderstorms based on Moran’s I . (a) 1961–1970, (b) 1971–1980, (c) 1981–1990, (d) 1991–2000, (e) 2001–2010, and (f) 1960–2010. Advances in Meteorology 9 Table 4: +e coverage percentage of spatial autocorrelation pattern of Iran’s thunderstorms based on Moran’s I. Pattern 1961–1970 1971–1980 1981–1990 1991–2000 2001–2010 1961–2010 HH 9.9 19.07 16.01 17.19 14.58 15.66 LL 2.79 16.014 13.7 14.34 15.04 15.61 HL — — — — — — LH — — — — — — 4120000 4420000 4720000 5020000 5320000 5620000 5920000 4120000 4420000 4720000 5020000 5320000 5620000 5920000 0 500000 1000000 0 500000 1000000 (a) (b) 4120000 4420000 4720000 5020000 5320000 5620000 5920000 4120000 4420000 4720000 5020000 5320000 5620000 5920000 0 500000 1000000 0 500000 1000000 (c) (d) Figure 4: Continued. Latitude Latitude 30200000 30500000 30800000 31100000 31400000 31700000 30200000 30500000 30800000 31100000 31400000 31700000 Latitude Latitude 30200000 30500000 30800000 31100000 31400000 31700000 30200000 30500000 30800000 31100000 31400000 31700000 10 Advances in Meteorology W E 4120000 4420000 4720000 5020000 5320000 5620000 5920000 4120000 4420000 4720000 5020000 5320000 5620000 5920000 0 500000 1000000 0 500000 1000000 (e) (f) Figure 4: Spatial distribution of variance (contour) and cycles (shaded) of Iran’s thunderstorms during various periods. (a) 1961–1970, (b) 1971–1980, (c) 1981–1990, (d) 1991–2000, (e) 2001–2010, and (f) 1960–2010. Table 5: e area covered by Iran’s thunderstorms during di­erent periods. Annual Cycle 1961–1970 1971–1980 1981–1990 1991–2000 2001–2010 Cycle 1961–2010 (year) (percent) (percent) (percent) (percent) (percent) (year) (percent) 1–2 9 7.6 8.7 1.7 12.6 0–10 3.2 2–4 74.6 33.6 49.2 52.7 69.5 10–20 14.9 4–8 15 50.7 37.2 43.5 15.2 20–30 14.1 8–10 1.4 8.1 4.8 2.1 2.7 30–40 18.5 40–50 49.3 to 20 years (Table 5). Jahanbakhsh and Edalatdoust believe scientists have attributed these 2-year to 4-year cycles to El Nino-Southern Oscillation (ENSO), Quasi Binomial Oscil- that these cycles are the result of sunspot activities and the North Atlantic Oscillations (NAO) [60]. lation (QBO) of the large-scale atmospheric circulation pattern, orbital currents, and other climatic-oceanic pro- Taken together, in some regions of Iran, thunderstorms cesses [58, 59]. are in–uenced by many factors and indicate various patterns. Cycles in which the return period is as long as the studied In other parts, they follow a limited number of patterns. For period indicate the existence of a trend. ese cycles can be instance, there is no dominant pattern in the southern and observed in the form of black spots in all the periods central parts of the country. Also, in all the ‹ve studied (Figure 4). For example, in the ‹rst period (1961–1970), 1.4% periods, a great deal of variety can be observed in thun- of the country’s area (which is mainly located in the west) derstorms’ number of cycles. On the other hand, western has a trend, while in the second period (1971–1980), 8.1% of and northwestern parts of the country experienced various cycles during the ‹ve periods. us, in addition to external the area of the country has this trend (Table 5). Following short-term cycles, midterm cycles of 4 to 8 years dominate factors, the topography of this part (western and north- the analysis scale of periods (Table 5). In the second period western) of the country played a signi‹cant role in the type of (1971–1980), these cycles form a strip and cover 50.7% area cycles. Nevertheless, Figure 3 (the map related to analyzing of the country, mainly located in the inner parts. is is the the annual cycles) shows that almost half of the country has a highest coverage of the cycle among all the ‹ve periods cycle which is similar to the studied period. As a result, in (Figure 4). Moving toward the fourth and ‹fth periods terms of both spatial and temporal scales, although thun- (1991–2000 and 2001–2010), one can observe that the derstorms have experienced oscillations, they have also had number of short-term cycles decline and midterm cycles some trends. In all the periods, the highest variance is ob- become dominant, instead. In total, around 15% of the served in the place where the length of the cycle is equal to country’s area had thunderstorms with midterm cycles of 10 that of the studied period. Latitude 30200000 30500000 30800000 31100000 31400000 31700000 Latitude 30200000 30500000 30800000 31100000 31400000 31700000 Advances in Meteorology 11 W E 4120000 4420000 4720000 5020000 5320000 5620000 5920000 Longitude Figure 5: Regions in–uenced by Iran’s thunderstorms during the past 50 years based on cluster analysis (percent). Table 6: Descriptive statistics for regions in–uenced by Iran’s thunderstorms based on cluster analysis. Frequency of the region which had few Frequency of the region which had a Frequency of the region which had a Index thunderstorms and high coeŽcient of mid-number of thunderstorms and low great number of thunderstorms and low variation (region 1) coeŽcient of variation (region 2) coeŽcient of variation (region 3) Mean 5.3 10.3 20.6 Standard 0.8 2 3.8 CoeŽcient 25.6 19.8 12.6 Maximum 6.9 15.7 31.2 Minimum 2.8 6.9 15.8 Skewness 0. 3 0.5 −0.02 Kurtosis 2 2.6 2.5 Range 4 8.8 15.4 Median 5.3 10 19.4 3.3. Analyzing understorms in Each Region. Cluster thunderstorms (20.6 days) (Table 6). On the other hand, the highest coeŽcient of variation (25%) belongs to the ‹rst analysis was utilized in order to investigate the regions that were in–uenced by thunderstorms. e results yielded three region (this region has few thunderstorms and high co- regions that indicate the regions covered by each cluster eŽcient of changes). Table 6 indicates that there is an in- (percent) (Figure 5). e ‹rst region mainly includes central signi‹cant di­erence between the mean and mode of parts of Iran, especially the Loot Desert. e second region thunderstorms in these three regions, a phenomenon that mostly encompasses the south, southeast, and northeast of shows the normal distribution of thunderstorms in the three the country. As indicated in Table 6, this region had an regions. Skewness is positive in the ‹rst and second regions. average thunderstorm occurrence of 10.3 days. Finally, the Furthermore, for a large area of these two regions, the av- third region includes some northwestern regions and parts erage occurrences of thunderstorms are 5.3 days and 10.3 of coastal area of the Persian Gulf. is region has positive days, respectively. Nonetheless, for the largest area of the spatial autocorrelation and the maximum intensity of third region, the mean of thunderstorm occurrence is 20.6 days. anomalies. It also has the highest average occurrence of Latitude 30200000 30500000 30800000 31100000 31400000 31700000 12 Advances in Meteorology [58, 66]. Furthermore, Hartman et al. believed that 9.1 year 4. Conclusion and 5.5 year cycles of precipitation could be explained in the +understorms, which constitute a climatic component, are light of the NAO and cycles of 11.8 years could be attributed a key element in water cycle and atmospheric electricity. to sunspot activities [58]. Phase oscillations were observed in +ey reflect the unusual manifestation of solar energy and thunderstorm cycles of mountainous areas of the country; consist of different dimensions. In most of the cases, nonetheless, these regions have experienced high coefficient thunderstorms have limited power. However, sometimes, of variation in different periods. +us, while planning for they are influenced by humidity, convective pattern, in- industrial activities, experts must consider these issues into tegration pattern, and atmospheric instabilities, hence account. causing large cyclonic storms which spread over thousands +e results of thunderstorm cluster analysis yielded three of square kilometers of areas and/or strong and destructive regions: the first region mainly includes central parts of Iran, tornadoes [1, 61]. +is study focused on thunderstorms that especially the Loot Desert. +e second one mostly encom- had been recorded between 1961 and 2010 in 98 synoptic passes the south, southeast, and northeast of the country. stations, and this period was divided into five decades And, the third region covers the northwest of Iran and some (1961–1970, 1971–1980, 1981–1990, 1991–2000, and small coastal areas of the Persian Gulf. 2001–2010). In all the five periods, thunderstorms mainly occurred in Data Availability the southwest and northwest of Iran. +e same findings have been obtained by Salahi and Ghavidel et al. [52, 62]. +e +e meteorological data used to support the findings of this occurrence of thunderstorm in the western coasts of the south study can be found in http://aerology.ir/ and are available of Iran has higher frequency than the central and the eastern from the corresponding author upon request. +e data given regions, making it a potential area in this region for storm in figures and tables which are used to support the findings formation [63]. In the west part of the country, especially in of this study are also available from the corresponding northwest (Tabriz, Oroomieh, and Zanjan stations) and west, author upon request. thunderstorms have higher frequencies [62]. Nevertheless, in the third and fourth periods, the intensity of these thunder- Conflicts of Interest storms increased in the southern regions of the country. At the same time, central parts of Iran had negative anomaly, a finding +e authors declare no conflicts of interest. that verifies the results of the study of Ghavidel et al. [62]. +e occurrence of thunderstorms has experienced a Authors’ Contributions rising trend toward recent decades; however, it has had a lot of oscillations, most of which have occurred along moun- All authors contributed equally to this article. tains, especially in the northwest topographies of Iran. +ese findings are in line with those of Ahmadi et al., who con- Acknowledgments centrated on thunderstorms in the province of Khuzestan [64]. Iman Rousta is deeply grateful to his supervisor (Haraldur +e results of data analysis indicated that in recent Olafsson, Professor of Atmospheric Sciences, Department of decades, positive autocorrelation pattern of thunderstorms Physics, University of Iceland, Institute for Atmospheric has had a significant increase. On the contrary, negative Sciences and Icelandic Meteorological Office (IMO)) for his autocorrelation pattern of these thunderstorms has had little great support, kind guidance, and encouragement. +is work oscillation. However, the positive pattern has experienced a was financially supported by Vedurfelagid, Rannis, and declining trend in the southern areas of the country. +e Rannsoknastofa vedurfraedi. same findings were obtained by [61]. Changes that occurred in the climate and water resources of Iran during the past References years and their consequences (e.g., warming, precipitation reduction, frequent droughts, and the decline of ground- [1] G. M. 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Harmonic Analysis of the Spatiotemporal Pattern of Thunderstorms in Iran (1961–2010)

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Abstract

Hindawi Advances in Meteorology Volume 2019, Article ID 1612503, 14 pages https://doi.org/10.1155/2019/1612503 Research Article Harmonic Analysis of the Spatiotemporal Pattern of Thunderstorms in Iran (1961–2010) 1 2 3 Ali Akbar Sabziparvar , Seyed Hossein Mir Mousavi, Mostafa Karampour, 4 5 6,7 8 Mehdi Doostkamian, Esmaeil Haghighi , Iman Rousta , Haraldur Olafsson, 9 10 11 12 Md Omar Sarif , Rajan Dev Gupta, Md Moniruzzaman, Khairul Hasan, and Ali Ghasemi Department of Agriculture, Bu-Ali Sina University, Hamedan, Iran Department of Geography, University of Zanjan, Zanjan, Iran Department of Geography, Lorestan University, Khorramabad, Lorestan 6813833946, Iran Department of Geography, University of Zanjan, Zanjan 3879145371, Iran Department of Physical Geography, University of Tabriz, Tabriz, Iran Department of Geography, Yazd University, Yazd 8915818411, Iran Institute for Atmospheric Sciences, University of Iceland and Icelandic Meteorological Office (IMO), Bustadavegur 7, IS-108 Reykjavik, Iceland Department of Physics, University of Iceland, Institute for Atmospheric Sciences and Icelandic Meteorological Office (IMO), Bustadavegur 7, IS-108 Reykjavik, Iceland Geographic Information System (GIS) Cell, Motilal Nehru National Institute of Technology Allahabad, Prayagraj-211004, India Civil Engineering Department, and Member of GIS Cell, Motilal Nehru National Institute of Technology Allahabad, Prayagraj-211004, India Center for Space Science and Technology in Asia and the Pacific (CSSTEAP), Dehradun-248001, India Department of Civil and Environmental Engineering, Shahjalal University of Science & Technology, Sylhet-3114, Bangladesh, India Department of Physical Geography, University of Tabriz, Tabriz, Iran Correspondence should be addressed to Esmaeil Haghighi; s.haghighi1985@gmail.com and Iman Rousta; irousta@yazd.ac.ir Received 30 August 2018; Revised 9 December 2018; Accepted 23 December 2018; Published 11 February 2019 Academic Editor: Helena A. Flocas Copyright © 2019 Ali Akbar Sabziparvar et al. +is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. +e current study aimed at investigating cycles and the spatial autocorrelation pattern of anomalies of thunderstorms in Iran during different periods from 1961 to 2010. In this analysis, 50-year periods (1961–2010) of thunderstorm codes have been collected from 283 synoptic stations of Meteorological Organization of Iran. +e study period has been divided into five different decades (1961–1970, 1971–1980, 1981–1990, 1991–2000, and 2001–2010). Spectral analysis and Moran’s I were used to analyze cycles and the spatial autocorrelation pattern, respectively. Furthermore, in order to conduct the calculations, programming facilities of MATLAB have been explored. Finally, Surfer and GIS were employed to come up with the graphical depiction of the maps. +e results showed that the maximum of positive anomalies mainly occurred in the northwestern and western parts of Iran due to their special topography, during all the five studied periods. On the other hand, the minimum of negative anomalies took place in central regions of the country because of lack of appropriate conditions (e.g., enough humidity). Moran’s I spatial analysis further confirmed these findings as Moran’s I depicts the positive and negative spatial autocorrelation patterns in line with negative and positive anomalies, respectively. However, in recent decades, this pattern has experienced a declining trend, es- pecially in southern areas of Iran. +e results of harmonic analysis indicated that mainly short-term and midterm cycles dominated Iran’s thunderstorms. 2 Advances in Meteorology to find the reasons behind the occurrence of thunderstorms, 1. Introduction and his findings showed that thunderstorms have a biannual +understorm is a climatic phenomenon that can have cycle which has a weak correlation with the solar cycle. destructive effects on agriculture, civil facilities, structures, Furthermore, he discovered that the maximum number of and economy [1]. In addition, thunderstorms are usually thunderstorms happened in June [5]. Enno et al. used the accompanied by other climatic phenomena (like hail, heavy Mann–Kendall test in order to analyze the frequency trend rainfall, snow, and lightening), which can also claim the loss of thunderstorms in Baltic countries within the period of numerous human lives [2, 3]. Although natural disaster 1950–2004, and his results indicated a 24% decline in the cannot be prevented, its deleterious consequences can be number of days in which thunderstorms happened. He predicted and minimized through studying thunderstorms recorded a rate of 0.9 day decline for each decade [17]. +e over long-term periods. By so doing, the degree of the similar research projects conducted by others (Yu and Lee, vulnerability of structures and civil facilities can be de- Abhilash et al., Lolis, and Mastrangelo et al.) are some other termined, leading to the construction of stronger structures examples in this regard [18–21]. in future. Furthermore, studying thunderstorms will reduce Iran is located on the way of different air masses, has human causalities to a great extent [4, 5]. Many factors such different physiographic units, and encompasses various as topography, land cover, and atmospheric processes are geographical latitudes [22–27]. +erefore, it has favorable effective on the occurrence of thunderstorms [6]. +un- conditions for the occurrence of thunderstorms. In Iran, the derstorms may be the result of severe gradient between two northwest and southwest of the Zagros mountain range systems [7, 8]. A number of studies have indicated that experience two opposite conditions; that is, during the height plays a significant role in the onset and evolution of transition from cold to warm season, the number of storms [9]. However, several studies have been conducted in lightning in the northwest of Iran is greater than that of the this field. For example, Davis investigated daily variations of southwest. On the other hand, during the transition from thunderstorms in Heathrow airport, London. +e results warm to cold season, lightning in southwest outnumbers the indicated that the majority of thunderstorms occurred in the one in northwest [28]. +e majority of precipitation in Iran afternoons. Also, some of them happened at midnights [10]. is influenced by the Mediterranean pattern [26, 27, 29–35]. However, Callado and Pascual Berghaenel used different When this pattern is combined with the Sudanese low models to study the Mediterranean thunderstorms, and they pressure and faces the topography of southwest of Iran, argued that the high frequency of thunderstorms in Cata- forced convective climb and instability occur. +us, the lonia at the end of the spring and in summer can be at- humidity of Arabian Sea moves toward southwest and tributed to local mountains and maritime influences [11]. conditions become favorable for thunderstorms [8, 33, 36]. Davolio et al. simulated the intense convective precipitation At the same time, local instabilities during warm seasons, th th event of September 8 and 9 , 2002, in the southeast of especially in higher areas, intensify rain showers. However, France, and they believed that the existence of a midsize one cannot ignore the role of foreign cold front, which enters the country during the cold season, in creating lightning [2]. convective system before the approaching of cold front was responsible for this event [12]. Trentmann et al. concentrated On the other hand, spatial changes of Azores High pressure th on variations in convective precipitations of July 12 , 2006, have different influences on Iran’s climate. More precisely, in Central Europe and found that the maximum amount of this system moves toward north and east, withdraws from available convective energy for these precipitations was the south because the polar trough low pressure advances in registered early afternoon, and they also concluded that this area, and interacts with Gang low pressure. +ese topography played a significant role in the occurrence of phenomena cause instability and showers. Furthermore, the these precipitations [13]. Kunz et al. studied the frequency two cells of Sudanese low pressure and Mediterranean are trend of thunderstorm during 1974–2003 in southwest combined over Kuwait and south of Iraq, with their troughs Germany, and they showed that the annual frequency of being extended to the northwest of Iran. Also, a low pressure days with thunder and lightning had remained almost intact. cell is formed over the strait of Hormoz and the north of Arabian Peninsula. +ese phenomena cause warm weather However, the number of days with hail and its consequent damages had risen significantly [2]. Lin-Lin et al. focused on and humidity of the Sea of Oman and the Persian Gulf to changes in thunderstorms within different decades and their move over the region, hence making the conditions more annual distribution, and their results demonstrated that favorable for instability and lightning [8]. Considering all thunderstorm distribution varied in different seasons. +at these into account, various instability indices, such as is, while in May, most of the thunderstorms happened in the SWEAT, TTI SOI, KI, SI, CIN, and CAPE, and the pre- north and in September, the majority of them occurred in cipitation water (PW) index can provide good evaluations of the south [14]. Mohee and Miller used radar data and earth thunderstorms. Among these indices, the Showalter index surface data in North Dakota in order to come up with the (SI) has yielded better results. climatic features of thunderstorms, and their results sup- Accompanying thunderstorm (with lightning, tornado, ported the idea that thunderstorms mainly happened in the hail, winds, heavy precipitations and hazardous atmospheric afternoon and early morning in June [15]. Loginov et al. phenomena like turbulence, freezing, and wind sheering) studied the formation and variations of some thunderstorms makes considerable irrecoverable damages to natural and in Belarus, and their results revealed that the thunderstorms human environments. However, most of the studies in Iran had a lot of temporal and spatial variation [16]. Mic intended have used different research methodologies to investigate Advances in Meteorology 3 thunderstorms in the northwest and southwest of the 2 2πq n a � 􏽘 x cos􏼒 t􏼓 q � 1, 2, . . . , , (3) country. Until now, no comprehensive study has been i t n n 2 t�1 conducted to include all areas of the country. As a result, the present study aimed at investigating changes in the spatial 2 2πq pattern of Iran’s thunderstorms within the last five decades b � 􏽘 x sin􏼒 t􏼓 q � 1, 2, . . . , n, (4) i t n n (1961–2010). t�1 where q is the number of harmonics. For even series, there 2. Methodology will be q � n/2 harmonics, whereas for odd series, there will be q � (n− 1)/2 harmonics. In order to analyze spatial autocorrelation cycles and patterns +e variance of each frequency (wave) was calculated of anomalies of Iran’s thunderstorms, thunderstorm codes (91 through the following formula: to 99, 17, and 29) of a 50-year period were collected from 283 2 2 I f � a + b . synoptic stations of Iran’s Meteorological Organization 􏼁 􏼐 􏼑 (5) i i i (IRIMO). +ese data were prepared and validated by the IRIMO and have not been missing values during the studied In order to investigate dominant patterns of Iran’s statistical period. thunderstorms within decades, geostatistical methods of (Figure 1 and Table 1). Moreover, in order to come up spatial autocorrelation (Moran’s I) were used. +is pro- with a periodical analysis of thunderstorms, the study cedure helps discover whether the distribution pattern of the period was divided into five time intervals (1961–1970, studied phenomenon follows a cluster or random pattern. In 1971–1980, 1981–1990, 1991–2000, and 2001–2010). fact, in this procedure, Moran’s I is calculated, and Z score When it comes to climate-related data, greater record and significance level are used to evaluate the calculated lengths are more valid and reliable. +erefore, the re- index. +is type of analysis shows the regions in which the searchers selected those stations which have the greatest studied phenomenon has been clustered and has followed a record length (Figure 1). significantly different pattern compared to the same phe- First, with the aim of gaining a general understanding of nomenon in the surrounding areas. As previously men- thunderstorms, descriptive data were presented for each tioned, the preassumption of this type of analysis is that the period. +en, in order to gain a detailed understanding, studied phenomena are weighed; hence, the clusters which anomalies and center mean of thunderstorms for each pe- have similar size can be easily identified. On the other hand, riod were analyzed. Average centers, which are the average the patterns that do not form a cluster can be recognized too. gravity of spatial distribution, are defined by equations (1) Moran’s I is calculated through equation (6) or equation (7). and (2): Z score is calculated by equation (8). 􏽐 P X x − X i�1 i i X � , (1) I � 􏽘 w x − X􏼁 , c n (6) i.j i 􏽐 P i�1 i j�1 j≠i 􏽐 P Y where x is the amount of phenomenon on the i th cell, X i�1 i i Y � , (2) c n is the mean of that phenomenon, and w is the spatial 􏽐 P i.j i�1 i weight between the two phenomena (i and j). Also, where P is frequency of thunderstorms and X and Y are i i i 􏽐 w j�1 j≠i i.j 2 latitude and longitude, respectively. (7) S � − X , n− 1 In order to have a more accurate analysis of thun- derstorm changes within each decade, spectral analysis where n is the number of all phenomena. Standard z I is was utilized to analyze thunderstorm cycles for each calculated through the following formula: period. +e distribution of variance along all wavelengths I − E􏼂I 􏼃 i i of the time series is known as spectral analysis. In fact, the 􏽱���� � z � , harmonic analysis technique entails analyzing the vari- V􏼂I 􏼃 (8) ance of a time series. In spectral analysis, time series are first converted to frequency functions (in the form of 2 j�1,j≠1 And : V I � E I − E I E I � − . periodic functions with amplitude and frequency). In such 􏼂 􏼃 􏽨 􏽩 􏼂 􏼃 􏼂 􏼃 i i i i n− 1 functions, frequency indicates the time scale (cycles within a time unit) and amplitude represents variance in After conducting thunderstorms’ cycle analysis and this time scale. +erefore, in this technique, each of the spatial pattern analysis, in order to determine the number of waves is extracted, and their contribution to the total regions, cluster analysis is used. In fact, in this study, variance is assessed. +en, after determining the variance, “Ward’s Hierarchical Clustering” or the “method of mini- each of the waves is studied to see if they are statistically mum variance” analysis on average of thunderstorm oc- significant. In order to convert the time series to fre- currence was used for zoning and clustering Iran’s quency and calculate the harmonics, first, two parameters thunderstorms. +e method was used to group locations and should be calculated through equation (5) by equation (3) their distributions based on their similarity. Ward’s method and equation (4) [37]: looks at cluster analysis as a variance problem and computes 4 Advances in Meteorology W E 0 115 230 460 (kilometers) 46 48 50 52 54 56 58 60 62 Longitude Figure 1: Distribution pattern of the studied stations. Table 1: understorm’s code and its description (source: WMO However, since variance is big, this period has the highest code 4677 ). coeŽcient of variation. On the contrary, the lowest mean (6.86 days) belongs to the second period (1971–1980). Code Description Considering the periods and years, there are great dif- understorm, but no precipitation at the time of ferences between mean, mode, and median. is shows observation that thunderstorms followed dissimilar patterns in dif- understorm (with or without precipitation) during ferent times, hence not forming a normal distribution. e the preceding hour but not at the time of observation understorm, slight or moderate, without hail, but large di­erence of the quartile range further con‹rms this with rain and/or snow at the time of observation claim. CoeŽcient of skewness is positive for all periods, a understorm, slight or moderate, with hail at time of phenomenon that shows the inclination of thunderstorms observation. toward smaller values. is inclination has reached its understorm, heavy, without hail, but with rain peak during the ‹rst period (1961–1970). Kurtosis is used and/or snow at time of observation to describe the degree of sharpness (or –atness) of the understorm combined with dust/sandstorm at curve. When the kurtosis coeŽcient is positive, the fre- time of observation quency of extreme values above the mean in the data is 99 understorm, heavy with hail at time of observation greater than the extreme values less than the mean. If kurtosis is close to zero, the curve will be sharp. As ob- served, kurtosis value is positive for all periods. Studying Euclidean distances to evaluate dissimilarity between the the 25, 50, and 75 percentile rank shows that the ‹fth clusters. Ward’s algorithm, when implemented on a dataset, period (2001–2010) experienced a greater value compared establishes groups by minimizing the dissimilarity or the to other periods. total sum of squares. is algorithm calculates several On the other hand, the maximum number of thun- clusters at a level when intergroup similarity is maximized derstorms occurred in the same period (2001–2010), which while intragroup similarity is minimized [38–42]. indicates the wide range of di­erences in thunderstorms. It seems that the maximum number of thunderstorms has not randomly happened in this period; otherwise, it would not 3. Results in–uence the center mean of this period (this period had the e salient statistical features of thunderstorm of Iran for lowest mean). It can be concluded that, except the ‹fth di­erent periods (1961–1970, 1971–1980, 1981–1990, period (2001–2010) in which a considerable increase can be 1991–2000, and 2001–2010) have been extracted (Table 2). observed in the number of thunderstorms, the dominant Accordingly, the greatest spatial mean (15.36 days) for pattern of thunderstorms has experienced a rather stable thunderstorms happened in the ‹fth period (2001–2010). mechanism in all the other periods. Latitude Advances in Meteorology 5 Table 2: Spatial features of the frequency of Iran’s thunderstorms for different periods. Index 1961–1970 1971–1980 1981–1990 1991–2000 2001–2010 1961–2010 Average 7.7 6.8 10.5 13.6 15.3 10.9 Median 6.7 6.1 9.1 12.5 13.2 9.7 Mode 0.6 0.5 0.9 1.5 4.2 2.8 Variance 24.5 15.1 37.9 37.8 65 29 Standard 4.9 3.8 6.1 6.1 8 5.3 Coefficient 63.7 56.7 58.1 44.9 52.4 49.2 Range 31.1 20.1 30.3 32.5 34.9 28.3 Skewness 2.3 0.77 1 1 1 1.2 Kurtosis 9.9 2.8 3.3 3.7 3.3 4.3 Maximum 31.7 20.7 31.2 34.1 39.2 31.2 Minimum 0.6 0.5 0.9 1.5 4.2 2.8 Q1 4.5 3.6 5.8 9.5 9 7.1 Q2 6.7 6.1 9.1 12.5 13.2 9.7 Q3 8.9 9.5 13.2 16.2 19.9 12.6 3.1. Analyzing Anomalies and the Spatial Autocorrelation negative anomaly because of low humidity and lack of Pattern of ?understorms. In order to gain a more accurate convective climb. However, in the second period (1971– understanding of the changing pattern of thunderstorm 1980), positive anomalies significantly increased around 8% within each decade, mean, anomaly (the average of each year in comparison with the previous period, i.e., (1961–1970) in different periods relative to the average annual value of the (Table 2). As a result, the coastal areas of the Caspian Sea and whole of the period), and center of gravity (center mean) the Persian Gulf experienced positive anomaly during this period, a condition that can be explained in the light of high were drawn for each period (Figure 2). +e center of gravity focuses on the occurrence of climate phenomena in the humidity in these regions. In the same period (1971–1980), studied area. For example, if the focus of the points is in the the spatial coefficient of variation of thunderstorms in the northwest of Iran, this indicates that the spatial center of northwest of Iran had no significant change compared to the gravity of the storms is in the northwestern part of Iran. In first period (Figure 2). It, however, experienced up to 20% this procedure, for each period, thunder deviation from the increase in other areas, especially central parts of Iran. High normal distribution was calculated through the algebra map. coefficient of variation in these regions indicates that Some anomalies on earth are under the influence of thunderstorms had a lot of oscillations in these areas. On the random climate phenomena, while others are the conse- other hand, they become more or less stable toward the northwest of the country. quence of a particular pattern [43, 44]. For example, positive anomalies of middle latitudes are the result of ocean currents In all periods, the northeast areas of the country expe- rienced negative anomaly (Figure 2). In other words, those and El Nino-Southern Oscillation (ENSO) [45]. Widespread cold anomalies may be a symptom of a severe winter. Some areas had a fewer thunderstorms than the other areas. Some scientists use anomalies to identify normal and abnormal scientists believe that thunderstorms in this region occur natural disasters. For instance, some scientists claim that because of its topography, high altitude above sea level, and environmental problems, such as flood, storm, and drought, the systems that enter the region from north and south [8]. are caused by climatic oscillations, especially exacerbated However, in the third period (1981–1990), the intensity of temperature anomalies on earth [46–51]. positive anomalies reduced; hence, almost 75% of the Analyzing anomalies of various periods shows that most country’s area experienced negative anomaly (Table 3). But of the positive anomalies belong to the northwest and the amplitude of these anomalies oscillated between−14 and 16. +us, moving from the four corners of Iran toward southwest of the country (Figure 2). Alijani has demon- strated the occurrence of this maximum in his own study central parts, one can observe that the intensity of thun- derstorms declines, but their distribution increases. +is [33]. +ese positive anomalies may be the result of local conditions, especially topography [3, 52, 53], and/or dy- indicates that humidity plays a very important role in namic and synoptic factors [54]. For example, some re- creating thunderstorms. As mentioned earlier, the second searchers attribute this maximum anomaly to anticyclone maximum intensity of thunderstorms in all the periods is ridges over Russia that extend to the northwest of Iran and observed in the south and southwest of the country. Some increases thermal gradient in the region. In addition, low researchers believe that these thunderstorms are caused by pressure cells over the strait of Hormoz and the north of combined patterns. More precisely, the stretch of a ridge on Arabian Peninsula, heat transfer, and humidity of the Sea of the northwest of Africa to Scandinavia creates cold air on the Oman provide necessary conditions for instability and Mediterranean and deepens its trough. +is phenomenon strengthens the low pressure center of the eastern Medi- lightning in the region [8]. One of the necessary conditions for the occurrence of thunderstorms is the availability of terranean. As a result, the increase of thermal gradient over the north of Africa and Red Sea strengthens the Sudanese humidity [55]. +us, it is observed that despite their high coefficient of variation, the central parts of Iran have system; hence, this system moves toward the north and is 6 Advances in Meteorology –3 –4 –8 –10 4120000 4420000 4720000 5020000 5320000 5620000 5920000 4120000 4420000 4720000 5020000 5320000 5620000 5920000 0 500000 1000000 0 500000 1000000 (a) (b) –2 –4 –8 –10 –16 –14 4120000 4420000 4720000 5020000 5320000 5620000 5920000 4120000 4420000 4720000 5020000 5320000 5620000 5920000 0 500000 1000000 0 500000 1000000 (c) (d) 20 19 8 9 –1 –4 –6 –10 –11 –16 4120000 4420000 4720000 5020000 5320000 5620000 5920000 4120000 4420000 4720000 5020000 5320000 5620000 5920000 0 500000 1000000 0 500000 1000000 (e) (f) Figure 2: Spatial distribution of mean (contour), center of gravity (+), and anomalies (shaded) of Iran’s thunderstorms in different periods. (a) 1961–1970, (b) 1971–1980, (c) 1981–1990, (d) 1991–2000, (e) 2001–2010, and (f) 1960–2010. Latitude Latitude Latitude 30200000 30500000 30800000 31100000 31400000 31700000 30200000 30500000 30800000 31100000 31400000 31700000 30200000 30500000 30800000 31100000 31400000 31700000 Latitude Latitude Latitude 30200000 30500000 30800000 31100000 31400000 31700000 30200000 30500000 30800000 31100000 31400000 31700000 30200000 30500000 30800000 31100000 31400000 31700000 Advances in Meteorology 7 Table 3: +e percentage of country’s areas (studied stations) influenced by positive and negative anomalies of thunderstorms in different periods. Index 1961–1970 1971–1980 1981–1990 1991–2000 2001–2010 1961–2010 Negative 77.2 70 75.1 73.4 71.5 76.7 Positive 22.8 30 24.98 26.6 28.5 23.3 combined with the Mediterranean low pressure, thus is set at 0.05. +e results of Moran’s I analysis are presented causing instability and thunderstorms over Iran [8, 36]. in Figure 3. Studying changes in anomaly patterns on both peri- Some other researchers believe that these thunderstorms happen because of the front nature accompanying the ad- odical and annual scales show that according to Moran’s model, the maximum intensity of anomalies forms a vection of warm air in lower parts of the atmosphere, which happens in cyclone centers of 500 HPa [56]. +erefore, in the positive spatial autocorrelation. In other words, it is sur- third, fourth, and fifth periods (1981–1990, 1991–2000, and rounded by high value phenomena. On the other hand, the 2001–2010), anomalies have had more or less similar con- negative spatial autocorrelation pattern matches the ditions (Table 3). However, in the fourth period, spatial minimum of negative anomalies. However, except the first coefficient of variation of thunderstorms experienced fewer period (1961–1970) in which this pattern has been in- changes. For example, compared to the third period significant, the pattern experienced almost similar condi- (1981–1990), the thunderstorm coefficient of variation in the tions in other periods. For instance, in the first period, only southwest of the country experienced 40% decline in the 9.9% of the country’s area (mainly the northwest of Iran and southwest of the Caspian Sea) had positive spatial fourth period (1991–2000) (Figure 2). +ese changes had less oscillation in the south, southwest, and northwest of the autocorrelation (Table 4). At the same time, during this period, less than 3% of the country’s area formed low country (compared to other regions) during all the five periods. +is can be explained in the light of the appropriate clustering pattern (Table 4). +us, in none of the periods, conditions of these regions for the occurrence of thunder- HL (i.e., high values surrounded by low values) and LH storms. +e center of gravity was drawn for thunderstorms (i.e., low values surrounded by high values) patterns can be in order to identify their spatial behavior and regularity of observed. Since the second period (1971–1980), the area occurrence (Figure 2). +ese centers are highlighted with dominated by positive and negative spatial autocorrelation gray spots. As indicated, the direction and inclination of increased, hence experiencing little oscillation in other thunderstorms’ centers of gravity has a direct relationship periods. +e most dominant positive spatial autocorrela- with positive anomalies on the scale of both periods and tion pattern (around 20%) can be observed during the second period (1971–1980) (Table 4). Since then, high years. +at is, since in all the periods the maximum intensity of anomalies is located in the northwest and west of the clustering pattern experienced a significantly downward trend, which moves from the north to the south of Iran. In country, the centers of gravity are located on the northwest of Iran (Figure 2). +e density of these centers on a single contrast, negative spatial autocorrelation experienced some location during all periods shows that they follow a regular oscillations Figure 3. +e same findings have been obtained pattern. +e density and regularity of these centers are so by Araghi et al., hence verifying the results of this study important that even a small change in their location rep- [57]. In general, thunderstorms do not follow any partic- resents significant changes in thunderstorms. ular pattern in the largest area of the country. In order to display the dominant pattern of spatial distribution of Iran’s thunderstorms during various periods, 3.2. Analyzing Oscillations and Cycles of ?understorms. Moran’s I was employed (Figure 3). +e results of this data In order to gain a more accurate understanding of thun- analysis procedure indicate that whether the phenomena are derstorm changes within each decade, spectral analysis was randomly distributed or are clustered around a single point. used to assess cycles that dominate each period. In spectral If the value of Moran’s I is positive, it shows that the studied analysis, changes in time series are divided into parts that phenomenon is surrounded by similar phenomena and it is part of that cluster. On the other hand, if the value of have frequencies. +is procedure of data analysis is used to extract and analyze explicit and implicit oscillations with Moran’s I is negative, it means that the studied phenomenon is surrounded by dissimilar phenomena, which do not form various wavelengths. Figure 4 shows the spatial distribution of variances and cycles of thunderstorms. In this figure, a cluster. +is index is calculated based on a standardized score and interpreted in the light of the significance level. cycles are identified by colorful spectra and variances are shown in the form of contours. In this procedure, HH represents clusters with high It is observed that in all the periods, mainly short cycles values (i.e., positive spatial autocorrelation), LL shows of 2 to 4 years dominate Iran’s thunderstorms (in 95 percent clusters with low values (i.e., negative spatial autocorrela- level of significant). For example, in the first period tion), HL indicates lack of any cluster (i.e., a high value is surrounded by lower values), and LH shows that a low value (1961–1970), almost 75% of the country’s area had thun- derstorms with 2-year to 4-year cycles (Table 5). Most of the is surrounded by high values. For this type of analysis, the p-value 8 Advances in Meteorology HL N HH HL HH W E W E LL LH LL LH 0 100000 200000 300000 0 100000 200000 300000 (a) (b) HL HH HL HH W E W E LL LH LL LH 0 100000 200000 300000 0 100000 200000 300000 (c) (d) HL N HH HL HH W E W E LL LH LL LH 0 100000 200000 300000 0 100000 200000 300000 (e) (f) Figure 3: Spatial distribution of autocorrelation pattern of Iran’s thunderstorms based on Moran’s I . (a) 1961–1970, (b) 1971–1980, (c) 1981–1990, (d) 1991–2000, (e) 2001–2010, and (f) 1960–2010. Advances in Meteorology 9 Table 4: +e coverage percentage of spatial autocorrelation pattern of Iran’s thunderstorms based on Moran’s I. Pattern 1961–1970 1971–1980 1981–1990 1991–2000 2001–2010 1961–2010 HH 9.9 19.07 16.01 17.19 14.58 15.66 LL 2.79 16.014 13.7 14.34 15.04 15.61 HL — — — — — — LH — — — — — — 4120000 4420000 4720000 5020000 5320000 5620000 5920000 4120000 4420000 4720000 5020000 5320000 5620000 5920000 0 500000 1000000 0 500000 1000000 (a) (b) 4120000 4420000 4720000 5020000 5320000 5620000 5920000 4120000 4420000 4720000 5020000 5320000 5620000 5920000 0 500000 1000000 0 500000 1000000 (c) (d) Figure 4: Continued. Latitude Latitude 30200000 30500000 30800000 31100000 31400000 31700000 30200000 30500000 30800000 31100000 31400000 31700000 Latitude Latitude 30200000 30500000 30800000 31100000 31400000 31700000 30200000 30500000 30800000 31100000 31400000 31700000 10 Advances in Meteorology W E 4120000 4420000 4720000 5020000 5320000 5620000 5920000 4120000 4420000 4720000 5020000 5320000 5620000 5920000 0 500000 1000000 0 500000 1000000 (e) (f) Figure 4: Spatial distribution of variance (contour) and cycles (shaded) of Iran’s thunderstorms during various periods. (a) 1961–1970, (b) 1971–1980, (c) 1981–1990, (d) 1991–2000, (e) 2001–2010, and (f) 1960–2010. Table 5: e area covered by Iran’s thunderstorms during di­erent periods. Annual Cycle 1961–1970 1971–1980 1981–1990 1991–2000 2001–2010 Cycle 1961–2010 (year) (percent) (percent) (percent) (percent) (percent) (year) (percent) 1–2 9 7.6 8.7 1.7 12.6 0–10 3.2 2–4 74.6 33.6 49.2 52.7 69.5 10–20 14.9 4–8 15 50.7 37.2 43.5 15.2 20–30 14.1 8–10 1.4 8.1 4.8 2.1 2.7 30–40 18.5 40–50 49.3 to 20 years (Table 5). Jahanbakhsh and Edalatdoust believe scientists have attributed these 2-year to 4-year cycles to El Nino-Southern Oscillation (ENSO), Quasi Binomial Oscil- that these cycles are the result of sunspot activities and the North Atlantic Oscillations (NAO) [60]. lation (QBO) of the large-scale atmospheric circulation pattern, orbital currents, and other climatic-oceanic pro- Taken together, in some regions of Iran, thunderstorms cesses [58, 59]. are in–uenced by many factors and indicate various patterns. Cycles in which the return period is as long as the studied In other parts, they follow a limited number of patterns. For period indicate the existence of a trend. ese cycles can be instance, there is no dominant pattern in the southern and observed in the form of black spots in all the periods central parts of the country. Also, in all the ‹ve studied (Figure 4). For example, in the ‹rst period (1961–1970), 1.4% periods, a great deal of variety can be observed in thun- of the country’s area (which is mainly located in the west) derstorms’ number of cycles. On the other hand, western has a trend, while in the second period (1971–1980), 8.1% of and northwestern parts of the country experienced various cycles during the ‹ve periods. us, in addition to external the area of the country has this trend (Table 5). Following short-term cycles, midterm cycles of 4 to 8 years dominate factors, the topography of this part (western and north- the analysis scale of periods (Table 5). In the second period western) of the country played a signi‹cant role in the type of (1971–1980), these cycles form a strip and cover 50.7% area cycles. Nevertheless, Figure 3 (the map related to analyzing of the country, mainly located in the inner parts. is is the the annual cycles) shows that almost half of the country has a highest coverage of the cycle among all the ‹ve periods cycle which is similar to the studied period. As a result, in (Figure 4). Moving toward the fourth and ‹fth periods terms of both spatial and temporal scales, although thun- (1991–2000 and 2001–2010), one can observe that the derstorms have experienced oscillations, they have also had number of short-term cycles decline and midterm cycles some trends. In all the periods, the highest variance is ob- become dominant, instead. In total, around 15% of the served in the place where the length of the cycle is equal to country’s area had thunderstorms with midterm cycles of 10 that of the studied period. Latitude 30200000 30500000 30800000 31100000 31400000 31700000 Latitude 30200000 30500000 30800000 31100000 31400000 31700000 Advances in Meteorology 11 W E 4120000 4420000 4720000 5020000 5320000 5620000 5920000 Longitude Figure 5: Regions in–uenced by Iran’s thunderstorms during the past 50 years based on cluster analysis (percent). Table 6: Descriptive statistics for regions in–uenced by Iran’s thunderstorms based on cluster analysis. Frequency of the region which had few Frequency of the region which had a Frequency of the region which had a Index thunderstorms and high coeŽcient of mid-number of thunderstorms and low great number of thunderstorms and low variation (region 1) coeŽcient of variation (region 2) coeŽcient of variation (region 3) Mean 5.3 10.3 20.6 Standard 0.8 2 3.8 CoeŽcient 25.6 19.8 12.6 Maximum 6.9 15.7 31.2 Minimum 2.8 6.9 15.8 Skewness 0. 3 0.5 −0.02 Kurtosis 2 2.6 2.5 Range 4 8.8 15.4 Median 5.3 10 19.4 3.3. Analyzing understorms in Each Region. Cluster thunderstorms (20.6 days) (Table 6). On the other hand, the highest coeŽcient of variation (25%) belongs to the ‹rst analysis was utilized in order to investigate the regions that were in–uenced by thunderstorms. e results yielded three region (this region has few thunderstorms and high co- regions that indicate the regions covered by each cluster eŽcient of changes). Table 6 indicates that there is an in- (percent) (Figure 5). e ‹rst region mainly includes central signi‹cant di­erence between the mean and mode of parts of Iran, especially the Loot Desert. e second region thunderstorms in these three regions, a phenomenon that mostly encompasses the south, southeast, and northeast of shows the normal distribution of thunderstorms in the three the country. As indicated in Table 6, this region had an regions. Skewness is positive in the ‹rst and second regions. average thunderstorm occurrence of 10.3 days. Finally, the Furthermore, for a large area of these two regions, the av- third region includes some northwestern regions and parts erage occurrences of thunderstorms are 5.3 days and 10.3 of coastal area of the Persian Gulf. is region has positive days, respectively. Nonetheless, for the largest area of the spatial autocorrelation and the maximum intensity of third region, the mean of thunderstorm occurrence is 20.6 days. anomalies. It also has the highest average occurrence of Latitude 30200000 30500000 30800000 31100000 31400000 31700000 12 Advances in Meteorology [58, 66]. Furthermore, Hartman et al. believed that 9.1 year 4. Conclusion and 5.5 year cycles of precipitation could be explained in the +understorms, which constitute a climatic component, are light of the NAO and cycles of 11.8 years could be attributed a key element in water cycle and atmospheric electricity. to sunspot activities [58]. Phase oscillations were observed in +ey reflect the unusual manifestation of solar energy and thunderstorm cycles of mountainous areas of the country; consist of different dimensions. In most of the cases, nonetheless, these regions have experienced high coefficient thunderstorms have limited power. However, sometimes, of variation in different periods. +us, while planning for they are influenced by humidity, convective pattern, in- industrial activities, experts must consider these issues into tegration pattern, and atmospheric instabilities, hence account. causing large cyclonic storms which spread over thousands +e results of thunderstorm cluster analysis yielded three of square kilometers of areas and/or strong and destructive regions: the first region mainly includes central parts of Iran, tornadoes [1, 61]. +is study focused on thunderstorms that especially the Loot Desert. +e second one mostly encom- had been recorded between 1961 and 2010 in 98 synoptic passes the south, southeast, and northeast of the country. stations, and this period was divided into five decades And, the third region covers the northwest of Iran and some (1961–1970, 1971–1980, 1981–1990, 1991–2000, and small coastal areas of the Persian Gulf. 2001–2010). In all the five periods, thunderstorms mainly occurred in Data Availability the southwest and northwest of Iran. +e same findings have been obtained by Salahi and Ghavidel et al. [52, 62]. +e +e meteorological data used to support the findings of this occurrence of thunderstorm in the western coasts of the south study can be found in http://aerology.ir/ and are available of Iran has higher frequency than the central and the eastern from the corresponding author upon request. +e data given regions, making it a potential area in this region for storm in figures and tables which are used to support the findings formation [63]. In the west part of the country, especially in of this study are also available from the corresponding northwest (Tabriz, Oroomieh, and Zanjan stations) and west, author upon request. thunderstorms have higher frequencies [62]. Nevertheless, in the third and fourth periods, the intensity of these thunder- Conflicts of Interest storms increased in the southern regions of the country. At the same time, central parts of Iran had negative anomaly, a finding +e authors declare no conflicts of interest. that verifies the results of the study of Ghavidel et al. [62]. +e occurrence of thunderstorms has experienced a Authors’ Contributions rising trend toward recent decades; however, it has had a lot of oscillations, most of which have occurred along moun- All authors contributed equally to this article. tains, especially in the northwest topographies of Iran. +ese findings are in line with those of Ahmadi et al., who con- Acknowledgments centrated on thunderstorms in the province of Khuzestan [64]. Iman Rousta is deeply grateful to his supervisor (Haraldur +e results of data analysis indicated that in recent Olafsson, Professor of Atmospheric Sciences, Department of decades, positive autocorrelation pattern of thunderstorms Physics, University of Iceland, Institute for Atmospheric has had a significant increase. On the contrary, negative Sciences and Icelandic Meteorological Office (IMO)) for his autocorrelation pattern of these thunderstorms has had little great support, kind guidance, and encouragement. +is work oscillation. However, the positive pattern has experienced a was financially supported by Vedurfelagid, Rannis, and declining trend in the southern areas of the country. +e Rannsoknastofa vedurfraedi. same findings were obtained by [61]. Changes that occurred in the climate and water resources of Iran during the past References years and their consequences (e.g., warming, precipitation reduction, frequent droughts, and the decline of ground- [1] G. M. 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