Relationships between lightning and rainfall intensities during rainy events in Cyprus

Transcription

1 Adv. Geosci., 23, 87 92, 2 doi:.594/adgeo Author(s) 2. CC Attribution 3. License. Advances in Geosciences Relationships between lightning and rainfall intensities during rainy events in Cyprus S. Michaelides, K. Savvidou, and K. Nicolaides Meteorological Service, Nicosia, Cyprus Received: March 29 Revised: 2 June 2 Accepted: 28 July 2 Published: 25 August 2 Abstract. The objective this work is to study relationship between number lightning recorded by a network lightning detectors and amount rainfall recorded by network automatic rain gauges, during rainy events in Cyprus. This study aims at revealing possible temporal and spatial relationships between rainfall and lightning intensities. The data used are based on available records hourly rainfall data and associated lightning data, with respect to both time and space. The search for temporal and spatial relationships between lightning and rainfall is made by considering various time-lags between lightning and rainfall, and by varying area around rain gauge which associated lightning data set refers to. The methodology adopted in this paper is a statistical one and rainy events registered under European Project FLASH are examined herein. Introduction A measurement electrical activity thunderstorms is achieved by counting number lightning flashes and evaluating flash density within a certain area. Such lightning location systems have been operating all over Europe and Mediterranean since middle past century (at that time, recorded flashes were better known as spherics ). The ZEUS lightning system is a modern network 5 land-based lightning detector stations (Fig. ). One se land stations is in operation at Larnaka Meteorological Office, located on souast coast Cyprus, since 22. ZEUS has a very good coverage area Eastern Mediterranean; system is administrated and data are Correspondence to: S. Michaelides (silas@ucy.ac.cy) archived by National Observatory Ans (NOA), in Greece (see Lagouvardos et al., 29). The link between lightning and rainfall has been documented by researchers for many years (see Uman, 987, for a historical review lightning studies). Techniques for directly estimating rainfall from cloud-to-ground (CG) lightning observations have also been explored by previous investigators (see Piepgrass and Krider, 982). In thunderstorms, a correlation between temporal evolution lightning and rainfall is supported by many studies (e.g., Ezcurra et al., 22). In vast majority thunderstorms studied, it was generally postulated that an increase in rainfall corresponded to an increase in lightning activity. Furrmore, from a spatial point view, cloud-to-ground (CG) flashes are generally found to occur close to area where heaviest precipitation occurs (Soula et al., 998). Also, many studies reveal a time-lag a few minutes between lightning and rain initiation (Soula et al., 998) or between peak flash rate and maximum rain rate (Altaratz et al., 23). In a study by Lang and Rutledge (22), complexity lightning evolution was revealed by studying cloud lightning output as a function storm s kinematics and microphysics. Rivas Soriano et al. (2) studied relationship between CG lightning and convective precipitation over Iberian Peninsula during warm season; y realized that convective precipitation and CG lightning show a similar spatial distribution and demonstrated that quantification a relationship between CG lightning and surface precipitation is possible. Tapia et al. (998) have demonstrated potential use lightning as a short-term predictor flash floods produced by localized intense convection. For purpose identifying possible relationships between lightning and rainfall, lightning data (provided by NOA) concerning nineteen rainy events in Cyprus were spatially and statistically related to respective rainfall measurements acquired from rain gauge network Cyprus Published by Copernicus Publications on behalf European Geosciences Union.

2 study is a continuation and extension previously published results reached lar methodology (see Michaelides et al., 29). 88 S. Michaelides et al.: Relationships between lightning and rainfall intensities during rainy events in Cyprus 4 Fig. 2. Lightning data for event on 8 October 26. Fig. 2. Lightning data for event on 8//26. Fig. 2. Lightning data for event on 8//26. Fig.. The ZEUS lightning network. Fig.. The ZEUS lightning network. Meteorological Service. A scheme time-lag variation between lightning and rainfall, as well as a scheme area vari- e area ation Cyprus around bounded wear stations by which meridians associated lightning data set is referring to, was used. The use a time- 32 o and 34.5 o E and parallel 5.8 o N. The rainfall data are obtained from network thirty-three rain lag as described above is supported by observations and orological values Service adopted herein Cyprus are within(fig.3). acceptable Not range all (see rain gauges were used for since precipitation Gungle and Krider, measurements 26). were not always readily available due to The present study is a continuation and extension previouslymalfunctioning. published results reached rain gauge Lightning by usingdata a rectangular retrieved around from each station ZEUS is km. system he National methodology Observatory (see Michaelides Ans et al., 29). (NOA) in Greece (an assessment ZEUS system has been presented by Morales et al (27) and, more recently, ouvardos 2 (28) Data and Lagouvardos et al. (29)). The study area is area Cyprus bounded by meridians 32 and 34.5 E and parallel circles 34.4 and 35.8 N. The rainfall data are obtained from network ent study thirty-three was to identify rain gauges possible Meteorological relationships Servicebetween rainfall and lightning geographical Cyprus (Fig. area. 3). For Not all this purpose, rain gauges were a certain used formethodology every rainy event, since precipitation measurements were not was adopted and ain steps which are briefly described in following. always readily available due to gaps in data or rain gauge e hourly malfunctioning. rainfall data Lightning concerning data retrieved from twenty-eight ZEUS rain events and ightning system datasets were provided were by gared. National Observatory A relzeus Ans stware, developed by anor (NOA) in Greece (an assessment performance namely, ZEUS GAMA system has (Meteorological been presented by Morales Hazards et al., Analysis 27, Team, Department eteorology, and, more Faculty recently, by Kotroni Physics, and Lagouvardos, University 28, and Barcelona, Spain) was Lagouvardos et al., 29). in order to derive possible relationships between recorded rainfall at a lightning activity reported within a circular area. The above stware 3 Methodology The aim present study was to identify possible relationships between rainfall and lightning activity in a certain geographical area. For this purpose, a certain methodology was adopted and implemented, main steps which are briefly described in following. Fig. Fig Rainfall Rainfall stations stations with with precipitation precipitation more more than than 5mm, 5 mm, concerning concerning event on 8 October 26; radius defining area event Fig. 3. Rainfall 8//26; stations with radius precipitation defining area more around than 5mm, each station concerning is km. event 8//26; radius defining area around each station is km. At first, hourly rainfall data concerning nineteen rain events and associated hourly lightning datasets were gared. The relzeus stware, developed by anor FLASH partner, namely, GAMA (Meteorological Hazards Analysis Team, Department Astronomy & Meteorology, Faculty Physics, University Barcelona, Spain) was subsequently used, in order to derive possible relationships between recorded rainfall at a certain station and lightning activity reported within a circular area. The above stware tags lightning data (characterized by specific time and geographical location) to a specific area with rain station at its center and a variable radius (an example a case study is given in Figs. 2 and 3) providing a graphical representation relationship, as well as calculating correlation coefficient between two sets data. A time-lag between rainfall data and lightning activity within circle relevant station 5, and 5 min was also set. In present study, with a time-lag 5, and 5 min between rain and lightning data, rainfall recorded from 2: until 3: UTC was associated with lightning recorded in time period :55 2:55, :5 2:5 and :45 2:45 UTC, respectively. A filter was also applied to rainfall data, excluding from calculations those stations that recorded rainfall less than 5 mm per hour; this was adopted in an attempt to limit Adv. Geosci., 23, 87 92, 2

3 S. Michaelides et al.: Relationships between lightning and rainfall intensities during rainy events in Cyprus 89 Table. The six schemes resulting from various combinations radius and time lags used in present analysis. Time lag Radius 5 min min 5 min km 5-R -R 5-R 5 km 5-R5 -R5 5-R5 study to those cases with heavier precipitation which are most likely to be associated with lightning. After applying filter and by varying radius ( or 5 km) around rainfall measuring station and varying time-lag between rainfall and lightning activity (5, or 5 min), six schemes rainfall stations and lightning data were established and subsequently used with relzeus stware for extracting relationships. These six schemes are presented in Table. For each above six schemes, a regression line between amount rainfall and corresponding number lightning, as well as correlation coefficient, were calculated. An example results for a case study is shown in Fig. 4. (a) (a) 4 Results and discussion In its present form, stware that was developed does not attempt to fit any or relationship but a linear one; thus for each event, regression lines are fitted to rainfall and lightning data for a radius and 5 km and for a time lag (between accumulated rainfall and subsequent lightning strikes) 5, and 5 min, accordingly. A basic statistical analysis characteristics regression lines nineteen rainy events was performed concerning slope regression line and correlation coefficient as functions radius and time lag. 4. Slope regression line A linear regression was fitted to lightning/rainfall data and sign its slope is studied in this sub-section: a positive (negative) sign indicates that rainfall increases (decreases) with an increase in number lightning. Figure 5 shows that both positive and negative slopes were found for all six schemes Table. The slope regression lines for nine events was positive, while for five events it was negative, irrespective radius and time lag. In an attempt to explain why such negative slopes are possible with data and methodology used here, following considerations were made. The nineteen events studied here were classified into two groups: eleven belong to cold period (October to April) and eight belong to warm period (May to September). (b) Fig. 4. Relationship between lightning and rainfall on 8//2 Fig. 4. Relationship between lightning (b) and rainfall on 8 October 26 for radius km with time-lag (a) 5 min and (b) min. (a) 5min and (b) min. Fig. 4. Relationship between lightning and rainfall on 8//2 (a) 4 Results 5min and and (b) discussion min. 4 In Results its Thepresent classification and form, discussion se stware events isthat summarized was developed in Table 2. does not a but The a linear major synoptic one; thus futures for each associated event, with regression warm period lines are fitted In its events present are form, surface seasonal stware low pressure that was (monsoonal), developed associated with an upper level trough that can lead to local con- radius and 5km and for a time lag (between does accumu not at but lightning a linear one; thus for each event, regression lines are fitted vection. strikes) As for cold5, period and events, 5 three minutes, variants accordingly. A radius characteristics accompanying and frontal 5km depression regression and for were lines a time noted: lag five nineteen events (between for rainy accumu eve lightning slope which strikes) major regression synoptic 5, line characteristic and 5 correlation is minutes, a frontal depression invading area regression from lines west; one event nineteen with arainy even accordingly. coefficient as A fu characteristics lag. slope depression regression extending from line and east; andcorrelation five events incoefficient which as fu lag. 4. Slope (relatively) low regression pressure over line area is a manifestation presence a surface trough. 4. A linear Slope Gungle regression and regression Krider was (26) fitted line studied to lightning/rainfall relationship between lightning and rainfall for warm-season thunderstorms data and this sub-section: a positive (negative) sign indicates that rain A linear Florida, regression USA; y was concluded fitted to that alightning/rainfall linear relationship increase in number lightning. Figure 5 shows that data both and could be found since, in events y studied, re was a this roughly sub-section: constanta rain positive volume (negative) per cloud-to-ground sign indicates flash and that rainf increase refore in cloud-to-ground number lightning. can Figure be used to 5 shows estimatethat both p location and intensity convective rainfall under that Adv. Geosci., 23, 87 92, 2

4 g in mind that amount precipitation at station does not change with 8 adius, different distribution lightning within circle around station, might 8 9 S. Michaelides et al.: Relationships between lightning and rainfall intensities during rainy events in time Cyprus lag 5min e in change sign slope, from positive to negative and vice versa. For 6 time lag min n cold group cases mentioned above, it appears that, for se cold group events, 4 Cluster for Radius km g activity Table could 2. be Classification more concentrated events intoaround warm and coldstation. period systems. Also, for warm group 2 2 eems that a larger area is required to take all corresponding lightning into account, at lightning Type activity eventrelated to Event precipitation number (Figs. could 8, 9 and be ) ( -.2) (.2-.4) more spread away from (.4-.6) (.6-.8) (.8-) 8 Warm period events, 2, 3, 5, 6,, 4, 6 Cold period events 4, 7, 8, 9,, 2, 3, 5, 7, 8, 9 6 time lag min Fig. 6. The percentage events per time lag and for a radius km Number events Positive events values were lower and in two events values were higher. Negative Cluster for Radius 5km 2 Also, most events for 5km radius were associated with correlation 2 with values ranging between.8 to and.6 to.8 (Fig. 7). As time lag increase 5-R -R 5-R 5-R5 -R5 5-R5 Time lag for correlation coefficient thirteen events studied remain unchanged, w 8 events values were lower and in two events values were higher. Fig. 5. The slope regression lines for six schemes. 6 time lag min Fig. 5. The slope regression lines for six schemes. wear regime. Bearing in mind above, negative 2 tion coefficient slopes found in present study could be an indication that ( -.2) (.2-.4) (.4-.6) (.6-.8) (.8-) in cases studied here rainfall volume per lightning is 8 correlation coefficient were classified into five clusters; (-.2), (.2-.4), (.4- highly variable. Fig. 67. The percentage events per time lag and for atime radius lag min.8) and (.8-); In studying this choice correlations is arbitrary, between but can lightning be related andfig. rainfall, to very 7. The low, percentage low, medium, 5 km. events per time lag and for a radius 5km 4 ery good correlation, Soula and Chauzy respectively. (2) noted that positive cloud-to-ground 2 flashes are associated with higher rainwater volume than negative flashes events and per a time correction lag and on for basis a radius this finding km is im- shown from in Fig. ( station. -.2) 6. The (.2-.4) (.4-.6) (.6-.8) (.8-) activity related to precipitation could be more spread away ercentage f events proved were associated relationships with established. correlation In coefficients present ranging paper, From above, from it.8 is evident to and that best time lag between rainfall and t re has been no distinction between positive and negative.4. As flashes time and lag refore increases, negative values slopes for could correlation be, at least coefficient 4.2 Correlation 3 events coefficient data is lowest, namely, 5min. oughly unchanged, partly, ascribed while toin afive dominance events values negative were flashes Fig. lower 7. inand The in percentage one event events per time lag and for a radius 5km The values correlation coefficient were classified into higher. some cases. five clusters; (.2), (.2.4), (.4.6), (.6.8) and Katsanos et al. (27) discuss certain issues regarding (.8 ); this choice is arbitrary, but can be related to very difficulties in establishment a 4.3 linear Changes relationship be-correlatiotween lightning and rainfall. Their analysis focused From on above, tively. it is evident that best time lag between rainfall and t low, low, Coefficient medium, good with and verychange good correlation, radius respec- for same tim wet season and covered a wider area In data in Figs. is eastern 8, lowest, 9 Mediterranean. They concluded that a linear data, relationship for each between event are shown km is for shown radius in Fig. 6. The km majority (in purple) events and were for radius and, namely, values The 5min. percentage correlation events per coefficient time lag and between for a radius lightning rainfall and lightning was not possible. associated with correlation coefficients ranging from.8 to blue) and with a time lag Three events cold group had negative slope for and from 5,.2 and to 5min,.4. As respectively, time lag increases, values 5 km radius and positive for km 4.3 radius. Changes Two events Correlation for correlation Coefficient coefficient with 3 change events remained radius roughly for same tim warm group had negative slope for For kmtime radius lag unchanged, 5min and while with in fiveincrease events in values radius werefrom lower andto in 5km, and positive for 5 km radius. coefficient In Figs. 8, 9 decreased and, in one values nine event value events, was higher. correlation increased coefficient in five between and in lightning remai Bearing in mind that amount remained precipitation at station does not change with changing data, radius, for each roughly different event unchanged. Also, most events for 5 km radius were associated shown with for correlation radius coefficients km with (in values purple) ranging and for be- radius are distribution lightning within circle blue) around and with station, a time lag tween 5,.8 toand and 5min,.6 to respectively,.8 (Fig. 7). As time lag might be cause in change sign slope, from increases, values for correlation coefficient thirteen 5min and events with studied increase remain unchanged, in radius from while in four to 5km, positive to negative and vice versa. For example, For intime cold lag group cases mentioned above, it appears that, for se cold events values were lower and in two events values coefficient decreased in nine events, increased in five and in remai group events, lightning activity could be more concentrated around station. Also, for remained warm group roughly events, unchanged. From above, it is evident that best time lag between were higher. it seems that a larger area is required to take all corresponding lightning into account, implying that lightning 5 rainfall and lightning data is lowest, namely, min. 4 2 ( -.2) (.2-.4) (.4-.6) (.6-.8) (.8-) with values ranging between.8 to and.6 to.8 (Fig. 7). As time lag increase Fig. 6. The percentage events per time lag and for a radius km. Fig. 6. The percentage events per time lag and for a radius km 2 Also, most events for 5km radius were associated with correlation for correlation coefficient thirteen events studied remain unchanged, w 4 2 Cluster for Radius 5km Adv. Geosci., 23, 87 92, 2

5 For coefficient time lag in 5min, ten events, increase an in increase radius resulted in five in a decrease events, whereas correlation in rest for events coefficient in ten change was events, not significant. in an increase in five events, whereas in rest for events change was not It significant. be concluded from above that optimum radius for all three time lags is S. km. Michaelides et al.: Relationships between lightning and rainfall intensities during rainy events in Cyprus 9 It can be concluded from above that optimum radius for all three time lags is km Change per event for Change.2 per event for Radius 5km Radius km Radius 5km Radius km Change per events for Fig. Fig The The correlation correlation coefficient coefficient for each forevent each with event a with radius a radius km Fig. (in. purple) The Fig. correlation and. with Thecoefficient a correlation for coefficient each event with for each a radius event with km a(in radius purple) and with a radius km (in 5km purple) (in blue) and and with a time a radius lag 5min. 5 km (in blue) and a time km (in Fig. 8. The correlation coefficient for each event with a radius km (in purple) and with a radius purple) 5 andkm with (in blue) a radius and a time 5 km lag (in5 blue) min. and a time lag 5 min. lag 5 min. radius 5km (in blue) and a time lag 5min. Change per events for time lag minit should be noted here that by changing time lag from 5 to min, five events noted a.2 significant change in correlation coefficient; those 2//26, 5/7/26, 7/7/26, Change per events for time lag min 8//26 and three 29/9/25. events By noted changing a significant time lag change again, this in time from correlation to 5min, three.2.8 events noted a significant change in correlation coefficient, namely, on 4/8/25, 28/5/25 coefficient, namely, on 4 August 25, 28 May 25 and Radius 5km.6 and 6/2/26. Radius km.8 6 February Radius 5km.6 For For km radius, km eleven radius, events eleven exhibit events consistently exhibit high consistently values correlation.2 Radius km.4 coefficient: six high events values belong to correlation warm period coefficient: group and five six events events belong belong to cold period group. The remaining to warm eight period events exhibit grouplow andvalues five events correlation belong tocoefficient: coldtwo events belong to period warm period group. group Theand remaining six events belong eight events to cold exhibit period low group. values Fig. 9. The correlation coefficient for each event with a radius km (in purple) For and 5km with correlation radius, a twelve coefficient: events two events exhibit belong consistently to high warm values radius 5km (in blue) and a time lag min. Fig. 9. The Fig. correlation 9. Thecoefficient correlation for coefficient each event with for each a radius event km with(in a radius correlation purple) coefficient: period group four events and six belong events to belong warm to period group cold period and eight group. events belong and with a km (in purple) radius and 5km with (in blue) a radius and a time 5 km lag (inmin. blue) and ato time cold period For group. 5The kmremaining radius, twelve seven events exhibit eventslow exhibit values consistently events highbelong values to warm correlation period group coefficient: and three events four belong eventsto cold correlation lag min. coefficient; four period group. belong to warm period group and eight events belong For both to radii cold and period for all group. time lags, The eight remaining events show seven high events values exhibit correlation 4.3 Changes correlation coefficient with coefficient: four lowevents values belong to correlation warm period coefficient; group and four or events four events belong belong to change radius for same time lag cold period group. to warm period group and three events belong to cold period group. Summarising above, it is evident that majority convective events (6 out In Figs. 8, 9 and, values correlation coefficient For both radii and for all time lags, eight events show high 8) show high values correlation coefficient for km radius while majority between lightning and rainfall data, for each event are values correlation coefficient: four events belong to frontal events (8 out ) show high values correlation coefficient for 5km radius. shown for radius km (in purple) and for radius This result may warm reflect period size group (horizontal and dimensions) or four events cumulonimbus belong tocloud under 5 km (in blue) and with a time lag 5, and 5different min, conditions. cold period group. respectively. Summarising above, it is evident that majority For time lag 5 min and with increase in radius from convective events (6 out 8) show high values to 5 km, correlation coefficient decreased in nine correlation coefficient for km radius while majority events, increased in five and in remaining five it remained frontal events (8 out ) show high values roughly unchanged. correlation coefficient for 5 km radius. This result may For time lag min and with increase in radius, reflect size (horizontal dimensions) cumulonimbus correlation coefficient presented a decrease in ten cloud under different conditions. events, in five events presented an increase and in rest for events change was not significant. For time lag 5 min, increase in radius resulted in a 5 Conclusions decrease correlation coefficient in ten events, in The objective this study was to identify relationships an increase in five events, whereas in rest for events between lightning and rainfall data, adopting a statistical change was not significant. methodology. The data used were hourly rainfall data It can be concluded from above that optimum radius from rain gauge network Cyprus Meteorological for all three time lags is km. Service and corresponding ZEUS lightning data. The sta- It should be noted here that by changing time lag from tions with rainfall less than 5 mm per hour were removed and 5 to min, five events noted a significant change in correlation not considered for furr study. The rainfall stations were coefficient; those 2 October 26, 5 July 26, considered to be centre circles a radius and 7 July 26, 8 October 26 and 29 September 25. By 5 km, while time lag between number lightning changing time lag again, this time from to 5 min, within selected radius and rainfall recorded at Radius 5km Radius km Adv. Geosci., 23, 87 92, 2

6 92 S. Michaelides et al.: Relationships between lightning and rainfall intensities during rainy events in Cyprus station, were 5, and 5 min. The possible relationships were calculated by GAMA stware for a total 9 rain events. The fitted regression between rainfall and lightning data was considered to be linear. A number cases had a negative slope and an attempt was made to provide a rar qualitative explanation. The classification values correlation coefficient showed that, for a radius km, 42% events have values between.8 and, which was perfect correlation between rainfall and lightning data. This percentage decreases as time lag increases; 37% for time lag min and 32% for time lag 5 min. When radius 5 km is in effect, corresponding percentage was much smaller; 29%, 33% and 24%, respectively. Also, best correlation between rainfall and lightning data was achieved, for all three time lags, in case circle with a radius km, which is comparable to typical size thunderstorm cells developing over area. The events studied in present paper and especially those with negative slope, will be furr investigated by authors in future work. Such future work is planned to include a better selection convective type events by using rmodynamic criteria. Thermodynamic indices may also be used as independent variables in a multiple regression analysis. Acknowledgements. This study was undertaken within framework Project FLASH which is funded by European Union (Sixth Framework Programme, Contract No ). The authors wish to thank K. Lagouvardos and V. Kotroni National Observatory Ans, Greece for providing lightning data and Maria-Carmen Llasat and her collaborators from Meteorological Hazards Analysis Team, Department Astronomy & Meteorology, Faculty Physics, University Barcelona, Spain for making available relzeus stware. Edited by: A. Orphanou Reviewed by: D. Katsanos and anor anonymous referee References Altaratz, O., Levin, Z., Yair, Y., and Ziv, B.: Lightning activity over land and sea on eastern coast Mediterranean, Mon. Wear Rev., 3, 26 27, 23. Ezcurra, A., Areitio, J., and Herrero, I.: Relationships between cloud-to-ground lightning and surface rainfall during in Spanish Basque Country area, Atmos. Res., 6, , 22. Gungle, B. and Krider, E. P.: Cloud-to-ground lightning and surface rainfall in warm-season Florida thunderstorms, J. Geophys. Res.,, D923, doi:.29/25jd682, 26. Katsanos, D., Lagouvardos, K., Kotroni, V., and Argiriou, A.: Combined analysis rainfall and lightning data produced by mesoscale systems in central and eastern Mediterranean, Atmos. Res., 83, 55 63, 27. Kotroni, V. and Lagouvardos, K.: Lightning occurrence in relation with elevation, terrain slope, and vegetation cover in Mediterranean, J. Geophys. Res., 3, D28, doi:.29/28jd65, 28. Lagouvardos, K., Kotroni, V., Betz, H.-D., and Schmidt, K.: A comparison lightning data provided by ZEUS and LINET networks over Western Europe, Nat. Hazards Earth Syst. Sci., 9, 73 77, doi:.594/nhess , 29. Lang, T. J. and Rutledge, S. A.: Relationships between Convective Storm Kinematics, Precipitation, and Lightning, Mon. Wear Rev., 3, , 22. Michaelides, S. C., Savvidou, K., Nicolaides, K. A., and Charalambous, M.: In search for relationships between lightning and rainfall with a rectangular grid-box methodology, Adv. Geosci., 2, 5 56, 29, Morales, C. A., Anagnostou, E. N., Williams, E., and Kriz, J. S.: Evaluation Peak Current Polarity Retrieved by ZEUS Long-Range Lightning Monitoring System, IEEE Geosci. Remote S., 4, 32 36, 27. Piepgrass, M. V. and Krider, E. P.: Lightning and surface rainfall during Florida thunderstorms, J. Geophys. Res., 87, 93 2, 982. Rivas Soriano, L., de Pablo, F., and García Díez, E.: Relationship between convective precipitation and Cloud-to-Ground lightning in Iberian Peninsula, Mon. Wear Rev., 29, , 2. Soula, S. and Chauzy, S.: Some aspects correlation between lightning and rain activities in thunderstorms, Atmos. Res., 56, , 2. Soula, S., Sauvageot, H., Molinié, G., Mesnard, F., and Chauzy, S.: The CG lightning activity storm causing a flashflood, Geophys. Res. Lett., 25, 8 84, 998. Tapia, A. and Smith, J. A.: Estimation Convective Rainfall from Lightning Observations, J. Appl. Meteorol., 37, , 998. Uman, M. A.: The Lightning Discharge, Academic Press, 377 pp., 987. Adv. Geosci., 23, 87 92, 2