Document Type : Research Paper


1 Professor, Department of climatology, the University of Tabriz, Tabriz, Iran

2 Ph.D student, Department of climatology, the University of Tabriz, Tabriz, Iran


Extended Abstract
Lightning is one of the most fascinating climatic phenomena, which has not yet been fully understood. This phenomenon usually occurs during thunderstorms and at the times of electrical field failure in a variety of cloud-to-ground, cloud-to-cloud, and in-cloud or intra-cloud. The cloud-to-ground lightning which strikes the ground is one of the most important causes of mortality due to weather conditions. Lightning can also cause many financial losses such as damaging power lines and causing fire. Therefore, spatial distribution of lightning is essential in terms of energy and safety management. Furthermore, our community, which increasingly relies on information networks, helps to identify the areas prone to lightning in order to protect the information systems. Lightning activities vary widely on the spatial and temporal scales, and depend on local convectional activities to some extent. The knowledge of lightening activities was usually based on surface measurements over time, prior to the arrival of the satellites. But, the activity of these storms is not measured in places where there are no synoptic stations. Therefore, it is necessary to predict the exact location and the severity of convective storms based on the development and monitoring of the route for timely notification. This is because all measurements related to thunderstorms in Iran are recorded with a three hour interval in various codes and only at synoptic stations.
Materials and Methods
This study was carried out using lightning data recorded in space by LIS sensor in a period from January 1998 to December 2013 (16 years). Lightning imaging sensor (LIS) is installed on the TRMM satellite. The LIS sensor is an optical detector that measures light-induced and light-intensity variations in clouds in the range of 777 nm/s and is capable of observing thunderstorms with a scale of 3 to 6 km on a 600x600-km while the effective LIS efficiency is 90% at night and 70% at local noon time.
 At the first stage of data analysis, it should be determined that the data are randomly distributed or have a certain spatial trend. Some of geographic processing functions were applied to data in the GIS software to compute the statistical values and to determine the locations having significant lightning levels. These calculations are done based on the Euclidean distance between the points (thunder and lightning) and the spatial concept of that weighting method based on the inverse distance. Other indices also compute the spatial distribution of the data. The nearest neighbor index (NNI) and the Kernel density function are among these indices. The NNI is expressed as a proportion of the observed distance to the expected distance, assuming the random distribution of the images. To generalize the geographic location of a phenomenon (lightning occurrence) to the whole area, the Kernel density interpolation estimator has been used throughout the region. In fact, the Kernel density function in the GIS software calculates the density of the features in their neighborhood and can be used for linear and point features (lightning).
Results and Discussion
The results of this research showed that the maximum frequency of lightning occurs in the southeast of Iran in the months of March to August (warm period of the year). Its highest frequency is in August and its lowest frequency is in December. In the study of daily changes it was also found that from the early afternoon until late afternoon (from 1300 to 1600 hours local time), the lightning activities significantly increase, which seems to be related to local convectional activities which are along with the surface heat  created by daily radiation of the sun. The nearest neighbor index results showed that the data distribution follows the cluster pattern. In other words, some regions have more favorable conditions for lightning. The results of the Kernel density index indicated that these areas are in the southern slopes of the region and its maximum is located before the main peak. The maximum frequency of lightning lies between 26° and 27° N, and is on the same orbital direction. Given the maximum lightning occurrence time which is during the warm period of the year, it seems that the southern currents created by the monsoons of the Southeast Asia along with local topography, is the exacerbating factor for the lightning activities in the southeast of Iran, and in particular, the region with a maximum lightning activities.
The use of satellite data to illustrate the distribution of some climatic phenomena can be very useful, since the frequency of some phenomena (especially lightning) is not recorded on ground stations. On the other hand, the distribution and density of ground stations are not appropriate, because the density of synoptic stations is particularly in low mountainous regions and the shape of the land in these areas is complex, and the distribution of thunderstorms is affected by this form of land. As it was observed in the results, one of the most important factors of the frequency distribution of lightning is the roughness, and these results indicate that remote sensing technology can be used to calculate the distribution of the phenomena of interest with high precision.


1- رسولی، علی اکبر، (1384)، تحلیلی بر فناوری GIS، دانشگاه تبریز، اول، ص 408.
2- رسولی، علی اکبر، (1390)، مقدمه‌ای بر هواشناسی و اقلیم شناسی ماهواره‌ای، دانشگاه تبریز، اول، ص 455.
 3- Albrecht, R. I. , S. J. Goodman, W. A. Petersen, D. E. Buechler, E. C. Bruning, R. J. Blakeslee, H. J. Christian (2011), The 13 years of TRMM Lightning Imaging Sensor: From individual flash characteristics to decadal tendencies, XIV International Conference on Atmospheric Electricity, August 08-12, 2011, Rio de Janeiro, Brazil.
4- Ashley,W. S. and Gilson, C.W., (2009), A Reassessment of U.S. LightningMortalities, B. Am. Meteorol. Soc., 10, 1501–1518.
5- Buechler, D. E. , W. J. Koshak, H. J. Christian, S. J. Goodman(2014), Assessing the performance of the Lightning Imaging Sensor (LIS) using Deep Convective Clouds, Atmospheric Research , PP 397–403.
6- Cecil, D. J., D. E. Buechler, R. J. Blakeslee,(2014) , Gridded lightning climatology from TRMM-LIS and OTD: Dataset description, Atmospheric Research, PP 404-414.
7- Christian, H. J., R. J. Blakeslee, D.J. Boccippio, W. L. Boeck, D. E. Buechler, K. T. Driscoll, S. J. Goodman, J. M. Hall,W. J. Koshak, D. M. Mach, and M. F. Stewart(2003), Global frequency and distribution of lightning as observed from space by the Optical Transient Detector, JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 108, NO. D1, 4005, doi:10.1029/2002JD002347.
8- ESRI (2005) The ESRI guide to GIS analysis, volume 2. ESRI press.
9- Feudale, L. Manzato, A. and Micheletti, m., (2013), A cloud-to-ground lightning climatology for north-eastern Italy, Adv. Sci. Res., 10, 77–84.
10- Gatrell AC (1994) Density estimation and the visualization of point patterns in Hearnshaw H J and  Unwin D J  eds  Visualization in geographical information systems  John Wiley, Chichester 65–75.
11- Haklander, A., J. and Delden, A., V.,(2003), Thunderstorm predictors and their forecast skill for the Netherlands, Atmospheric Research 67– 68, pp 273– 299.
12- Hodanish, S., Wolyn, P, (2012), Lightning Climatology for the State of Colorado, , 23rd International Lightning Detection Conference, 2-3 April, Broomfield, Colorado, USA, 4th International Lightning Meteorology Conference, 2-3 April, Broomfield, Colorado, USA.
13- Huffines, G. R. and Orville, R. E. (1999), Lightning Ground Flash Density and Thunderstorm Duration in the Continental United States: 1989–96. J. Appl. Meteor., 38, pp1013–1019. American Meteorological Society.
14- Illian J, Penttinen A, Stoyan H and Stoyan D (2008), Statistical analysis and modeling of spatial point patterns. Wiley, London.
15- Kilinc, Musa and Jason Beringer, (2007), The Spatial and Temporal Distribution of Lightning Strikes and Their Relationship with Vegetation Type, Elevation, and Fire Scars in the Northern Territory. J. Climate, 20, 1161–1173.
16- Kodama, Y.M.  Haruna Okabe, Yukie Tomisaka, Katsuya Kotono, Yoshimi Kondo, and Hideyuki Kasuya, (2007): Lightning Frequency and Microphysical Properties of Precipitating Clouds over the Western North Pacific during Winter as Derived from TRMM Multisensor Observations. Mon. Wea. Rev., 135, 2226–2241.
17- Liu, C., and E. J. Zipser (2008), Diurnal cycles of precipitation, clouds, and lightning in the tropics from 9 years of TRMM observations, Geophysics Res. Lett., 35, L04819, doi: 10.1029/2007GL032437.
18- Morales, C. A.,  J. R. Neves , E. A. Moimaz , K. S. Camara ,(2014), Sferics Timing And Ranging Network – STARNET:  8 years of measurements in South America, XV International Conference on Atmospheric Electricity, 15-20 June 2014, Norman, Oklahoma, U.S.A.
19- NASA, (2000), Science data validation plan for the Lightning Imaging Sensor (LIS),  Earth Science Department, Science Directorate NASA/Marshall Space Flight Center Global Hydrology and Climate Center Huntsville, AL 35812.
20- Petersen, W. A. , S W. Nesbitt, R. J. Blakeslee, R. Cifelli, P. Hein, S. A. Rutledge(2002), TRMM Observations of Intraseasonal Variability in Convective Regimes Over the Amazon, Volume 15, Issue 11, PP1278-1294.
21- Rasuoli, A. A.(1996), The Temporal and Spatial Study of Thunderstorm Rainfall in the Greater Sydney region, A Thesis submitted in fulfillment of the requirements for the award of the degree Doctor of Philosophy from University of Wollongong.
22- Rorig, M. and Ferguson, S., (1999), Characteristics of lightning and wild-land re ignition in the Pacic Northwest, J. Appl.Meteorol., 38,1565-1575.
23- Rudlosky, S. D. (2014), Evaluating Ground-Based Lightning Detection Networks using TRMM/LIS Observations, 23rd International Lightning Detection Conference, 18-19 March, Tucson, Arizona,  USA, 5th International Lightning Meteorology Conference, 20-21 , Tucson, Arizona,  USA.
24- Scott TS, Graffman I and Ingram J (2000) GIS Applications in climate and meteorology.  ESRI International User Conference.
25- Ushio, T, Satoru Yoshida , Syunsuke Sakurai , Zen-Ichiro Kawasaki , and Ken’ichi Okamoto(2012), On the relationship between radar reflectivity factor and thunderstorm flash rate, Journal of Geophysical Research Atmospheres (Impact Factor: 3.44). 03/2012; 117(D6):6212-. DOI: 10.1029/2011JD017123.
26- Xiushu, Q, Z. Yunjun, Y. Tie, (2003), Global Lightning Activities And Their Regional Differences Observed From Satellite, Chinese Journal Of Geophysics, Vol.46, No.6, pp: 1068-1077.