Spatio-temporal Distribution of Lightning Phenomenon in Iran Using TRMM Lightning Image Sensor (LIS) Data

Document Type : Research Article

Authors

• Ali Mohammad Khorshiddoust ¹
• Ali Akbar Rasouly ²
• Mojtaba Fakhari Vahed ¹

¹ Tabriz university

² tabriz university

10.22067/geo.v6i1.53347

Abstract

1. Introduction
Lightning is a sudden electrostatic discharge during an electrical storm between electrically charged regions of a cloud (called intra-cloud lightning), between two clouds (CC lightning), or between a cloud and the ground (CG lightning).This phenomenon is one of the most important featureswhich are associated with extreme storms and seize the life of about 2,000 people in the world each year.The occurrence of lightning is related to the cloud microphysics in the mixed-phase layer becauselightning is frequent in convective clouds that contain many large hydrometeors in the mixed-phase layer.Also, air on the windward side of a mountain is forced to rise; anditoften leads tothecloudand lightning.In fact, lightning activities are highly variable on many spatial and temporal scales, and to some extent depend on the local convective regime.Lightning activities are not registered in the synoptic stations, but the thunder day statistics determined by human observers and compiled by the World Meteorological Organization (WMO) are one of the best sources of proxy information concerning lightning activity worldwide.Lightning day might include more than one hundred events; therefore, it cannot be a good representative for the lightning activitywhilethese stationsdo notshow offa good distribution.Accordingly, using remote sensing technology can be accurately measured lightning activity. Several space borne instruments have measured the global distribution of lightning one of which is Lightning Image Sensor (LIS).In this paper, diurnal, spatial, and temporaldistribution of the lightning phenomenon in Iran werestudied using LIS data.

2. Study Area
Iran, with an area of 1,648,195 km2 is located in the southern part of the temperate zone of the northern hemisphere.It is situated between 25° and 47°northern latitudes, and 44° to 63° eastern longitudes.This area is generallymountainous and semi-arid.The lowest area is 28 m lower than sea level located on northern Iranand its highest peak is Damavand with an altitude of 5,671 m/ASL.Existence of this diversity in roughness of the ground causes different climatic characteristics in various parts of the country.


3. Material and Methods
Two satellite-based lightning sensors have been successfully used by NASA since April1995. The sensors can detect the total lightning activities (cloud-to-ground flash and intra-cloud flash) on a global scale. One of these sensors is Lightning Image Sensor (LIS) which was launched on November 28, 1997 aboard the Tropical Rainfall Measuring Mission (TRMM). The LIS sensor detects lightning with storm-scale resolution of 3∼6km over a region of 600km×600kmof the earth’s surface. The LIS circles the earth with a velocity of 7km·s−1, and observes a point on theearth or a cloud for about 90s. This short sampling time during the satellite overpass limits the data usage for forecast and requires several years to compute high resolution climatology. Nowadays, LIS has collected lightning measurements for over 16 years making possible the compilation of total lightning climatology maps in high resolution such as 0.250 and 0.100 of horizontal resolution. In this paper, diurnal, spatial and temporal distribution of the lightning phenomenon in Iran werestudied using LIS data. Some geoprocessing functions in ArcGIS were applied tocalculate statistical values and to identify the locations of statistically significant lightning clusters. For generalizing geographic locations of lightning occurrence to an entire area a Kernel Density Interpolation estimator was introduced. Basically, a Kernel density tool calculates the density of point features such as lightning occurrence locations in a radius searcharound all similar features. Conceptually, a smooth, curved surface is fitted over each lightning flash pointincidentin Kernel density procedure regarding all observations. The surface value is highest at the location of the occurrence point and diminishes with increasing distance from the point, reaching zero at the search radius distance from the point. In practice, the density rate at each output raster cell is calculated by adding the values of all the Kernel surfaces where they overlay the raster cell centre, based on a quadratic Kernel function.

4. Results and Discussion
The result showed that diurnal cycle of lightning display a local maximum in flash rate in early afternoon (between 12 and 15 local time) and local minimum in flash rate in early morning to late morning (between 01 and 11 local time). Monthly variation of lightning indicated that maximum frequency of lightning occurs in April whereas the minimum happensin January and September. Annual distribution of lightning data indicated that the maximum frequency of lightning coincides with mountain areas. A majority of the lightning activities over the mountain region occurs primarily in southern slopes ofthe mountains. More specifically, this maximum occurs over the south and southeast facing slopes of the mountainous areaslikeZagros, Alborz, Binalud, Barez, etc. Western and south-western slopes of the Zagros Mountains have the highest rate of annual lightning in Iran.Central regions of Iran have the lowest frequency of lightningwhich are generally flat and arid.
The result of Kernel density function showed that distribution of lightning in January, November and December are alike and maximum density of lightning occurs in southwest of Iran (between Khozestan and Lorestan provinces). The maximum density of lightning in February, March and October are also in southwest of Iran but the lightning occurred in a wider area. The peakfrequency oflightningactivityoccurs inApril and Mayanditsspreadismuch more thanother months. In these months, west, southwest and northeast of Iran have maximum frequencies of lightning. In June, July, August and September, the distribution of lightning activities are different from other months and the maximum density of lightning are in southern Kerman, Sistan and Baluchestan and some areas of Hormozgan province.

5. Conclusion
Although lightning activity occurs in all regions, it appears that some areas havemore favorable conditions for the occurrence of this phenomenon. This study investigated diurnal, spatial and temporal distribution of lightning activity with 16 years (1998–2013) of LIS.The results provide valuable information on the distribution of lightning activity in Iran, sinceno study had been carried outbefore the distribution of this phenomenon in Iran.Results of diurnal cycle indicated that there was a marked daily distribution of lightning frequency during the afternoons peaking between 3PM and 5PM hours. These results nearly match the pervious findings; in such studies it was shown that all maximums in lightning were observed during the afternoons between 3pm and 7pm (EST). The increase in storms during this period is primarily due to the proliferation in energy provided by the sun during the warmer spring and summer months. The monthly distribution of lightning showed a distinct tendency indeed for all lightning to occur during March to May.The increase instorms during this period is primarily due to the increase in energy provided by the sun during the warmer spring. The result of lightning distribution analysis indicated that a majority of the lightning activity over the mountain region occurs primarily over the southern slopes ofthe mountains. Western and south-western slopes of the Zagros Mountains have the highest rate of annual lightning in Iran. Maximum frequency of lightning in January, February, March, October, November and December are also in this region but in warm season (June, July, August and September), south and southeast of Iran have maximum frequency of lightning activity.

Keywords

• Lightning
• LIS
• Climatology
• Thunderstorm
• Iran

---

References

بختیاری، ناصر؛ 1390. تحلیل آماری همدید توفان‌های تندری در استان فارس. پایان‌نامه کارشناسی ارشد. دانشگاه پیام نور مرکز اصفهان. اصفهان.

جلالی، اروج، رسولی، علی اکبر، ساری صراف، بهروز؛ 1385. توفان‌های تندری و بارش‌های ناشی از آن را در محدوده شهر اهر. مجله جغرافیا و برنامه‌ریزی. شماره 24. صص 33-18.

خالصی، فریده؛ 1393. واکاوی زمانی توفان‌های تندری در ایران، آب و هواشناسی کاربردی. جلد 1. شماره 1. صص 47-60.

رسولی، علی اکبر؛ 1384. تحلیلی بر فناوری GIS. تبریز: انتشارات دانشگاه تبریز.

رسولی، علی اکبر؛ 1390. مقدمه ای بر هواشناسی و اقلیم شناسی ماهواره ای. تبریز: انتشارات دانشگاه تبریز.

رسولی، علی اکبر، بداق جمالی، جواد، جلالی، اروج؛ 1386. توزیع زمانی بارش های رعدوبرقی منطقه شمال غرب ایران، مجله پژوهشی علوم انسانی دانشگاه اصفهان، شماره 1، پیاپی 22، صص 170-155.

عسگری، احمد، محبی، فرشته؛ 1389. مطالعه آماری- همدیدی توفآن‌های تندری در استان خوزستان، چهارمین کنفرانس منطقه‌ای تغییر اقلیم، 29 آذر تا 1 دی، تهران، صص 111- 119.

علائی طالقانی، محمود؛ 1392. ژئومرفولوژی ایران، تهران، انتشارات قومس.

علیجانی، بهلول؛ 1389. آب و هوای ایران، چاپ دهم، تهران، انتشارات پیام نور.

قاسمی دستگردی، احمدرضا؛ 1391. مدل‌سازی تغییرات زمانی و مکانی پوشش ابری با تاکید بر روزهای بارش‌ در ایران، پایان نامه دکتری، تبریز، دانشگاه تبریز.

قویدل رحیمی، یوسف، فرج زاده، منوچهر، باغبانان، پرستو؛ 1394. روند تغییرات زمانی توفان‌های تندری در ایران، برنامه ریزی و آمایش فضا، دوره 19، شماره 2، صص 210-185.

لشکری، حسن (1381. مسیریابی سامانه‌های کم فشار سودانی ورودی به ایران، مجله مدرس، دوره 6، شماره 2، صص156- 133.

مسعودیان، ابوالفضل و کاویانی، محمدرضا؛ 1386. اقلیم‌شناسی ایران، اصفهان، انتشارات دانشگاه اصفهان.

Albrecht, R. I., Goodman, S. J., Petersen, W. A., Buechler, D. E., Bruning, E. C., Blakeslee, R. J., & Christian, H. J. (2011). The 13 years of TRMM lightning imaging sensor: From individual flash characteristics to decadal tendencies.XIV International Conference on Atmospheric Electricity, Rio de Janeiro, Brazil.

Buechler, D. E., Koshak, W. J., Christian, H. J., & Goodman, S. J. (2014). Assessing the performance of the lightning imaging sensor (LIS) using deep convective clouds. Atmospheric Research, 135, 397-403.

Cecil, D. J., Buechler, D. E., & Blakeslee, R. J. (2014). Gridded lightning climatology from TRMM-LIS and OTD: Dataset description. Atmospheric Research, 135, 404-414.

Christian, H. J., Blakeslee, R. J., Boccippio, D. J., Boeck, W. L., Buechler, D. E., Driscoll, K. T., ... & Stewart, M. F. (2003). Global frequency and distribution of lightning as observed from space by the optical transient detector. Journal of Geophysical Research: Atmospheres, 108(D1), 1-14.

Christian, H. J., Blakeslee, R. J., Goodman, S. J., & Mach, D. M. (2000). Algorithm theoretical basis document (ATBD) for the lightning imaging sensor (LIS). NASA/Marshall Space Flight Center, Alabama.

Haklander, A. J., & Van Delden, A. (2003). Thunderstorm predictors and their forecast skill for the Netherlands. Atmospheric Research, 67, 273-299.

Hodanish, S., &Wolyn, P. (2012, April). Lightning climatology for the state of Colorado.Paper presented at the 23rd International Lightning Detection Conference & 4th International Lightning Meteorology Conference,Broomfield, Colorado, USA.

Huffines, G. R., & Orville, R. E. (1999). Lightning ground flash density and thunderstorm duration in the continental United States: 1989-96. Journal of Applied Meteorology, 38(7), 1013-1019.

Kilinc, M., &Beringer, J. (2007). The spatial and temporal distribution of lightning strikes and their relationship with vegetation type, elevation, and fire scars in the Northern Territory. Journal of Climate, 20(7), 1161-1173.

Kodama, Y. M., Okabe, H., Tomisaka, Y., Kotono, K., Kondo, Y., &Kasuya, H. (2007). Lightning frequency and microphysical properties of precipitating clouds over the western North Pacific during winter as derived from TRMM multisensor observations. Monthly Weather Review, 135(6), 2226-2241.

Liu, C., &Zipser, E. J. (2008). Diurnal cycles of precipitation, clouds, and lightning in the tropics from 9 years of TRMM observations. Geophysical Research Letters, 35(4), 311-326.

Morales, C. A., Neves, J. R., Moimaz, E. A., &Camara, K. S. (2014). Series Timing and Ranging NETwork – STARNET: 8 years of measurements in South America. XV International Conference on Atmospheric Electricity, Norman, Oklahoma, U.S .A.

Morita, J., Takayabu, Y. N., Shige, S., & Kodama, Y. (2006). Analysis of rainfall characteristics of the Madden–Julian oscillation using TRMM satellite data. Dynamics of Atmospheres and Oceans, 42(1), 107-126.

Petersen, W. A., Christian, H. J., & Rutledge, S. A. (2005). TRMM observations of the global relationship between ice water content and lightning. Geophysical Research Letters, 32(14), 1-4.

Petersen, W. A., Nesbitt, S. W., Blakeslee, R. J., Cifelli, R., Hein, P., & Rutledge, S. A. (2002). TRMM observations of intraseasonal variability in convective regimes over the Amazon. Journal of Climate, 15(11), 1278-1294.‏

Qie, X., Zhou, Y., &Yuan, T. (2003). Global lightning activities and their regional differences observed from satellite. Chinese Journal of Geophysics, 46(6), 1068-1077.‏

Rasuoli, A. A.(1996). The temporal and spatial study of thunderstorm rainfall in the greater Sydney region(Unpublished doctoral dissertation).‏University of Wollongong, New South Wales, Australia.

Rudlosky, S. D. (2014). Evaluating Ground-based lightning detection networks using TRMM/LIS observations, 23rd International Lightning Detection Conference & 5th International Lightning Meteorology Conference, Tucson, Arizona, USA .

Shipley, S. T., Graffman, I. A., & Ingram, J. K. (2000). GIS applications in climate and meteorology. In Proc., ESRI International User Conference. San Diego, California .

Ushio, T., Yoshida, S., Sakurai, S., Kawasaki, Z. I., & Okamoto, K. I. On the relationship between radar reflectivity factor and thunderstorm flash rate.