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Investigating the relationship of lightning activity and rainfall: a case study for Crete Island

Iordanidou Vasiliki, Koutroulis Aristeidis, Tsanis Giannis

Πλήρης Εγγραφή


URI: http://purl.tuc.gr/dl/dias/A8974D0B-61A3-49F2-A2EC-3B4618BF1F0C
Έτος 2016
Τύπος Δημοσίευση σε Περιοδικό με Κριτές
Άδεια Χρήσης
Λεπτομέρειες
Βιβλιογραφική Αναφορά V. Iordanidou, A. G. Koutroulis and I. K. Tsanis, "Investigating the relationship of lightning activity and rainfall: a case study for Crete Island," Atmos. Res., vol. 172-173, pp. 16-27, May 2016. doi: 10.1016/j.atmosres.2015.12.021 https://doi.org/10.1016/j.atmosres.2015.12.021
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Περίληψη

The relationship of lightning activity and rainfall is investigated for rain events of variable intensity. Rain data from 22 gauging stations over the island of Crete and lightning activity from the Global Lightning Network including both cloud-to-ground and some cloud flashes are analyzed for the period September 2012 to June 2014. Local thunderstorms' characteristics are investigated both individually as well as in groups according to the results of k-means clustering algorithm in 3 dimensions (space (x, y) and time (t)) in which the number of clusters is decided by G-means algorithm. Correlation of non-zero pairs of rain intensity and number of flashes is examined at various time intervals, time lags and effective radii. Also, correlation of flash count within 50 km radius around the stations is examined for the rain events of maximum hourly intensity for each gauging station. The highest coincidence of lightning clusters with intense rain events reaches 60% when gauges are 25-30 km from the cluster center. Maximum correlation within non-zero pairs of rain intensity and flashes number is obtained for more intense rain (99th percentile) and for increased flash count within the searching area (more than 10 flashes). Also, correlation is stronger for shorter time windows. The findings of this study improve the understanding of thunderstorm events and could provide staple information for the improvement of forecasting extreme events.

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