Surveys in Geophysics

, 32:733 | Cite as

Using Lightning Data to Better Understand and Predict Flash Floods in the Mediterranean

  • C. Price
  • Y. Yair
  • A. Mugnai
  • K. Lagouvardos
  • M. C. Llasat
  • S. Michaelides
  • U. Dayan
  • S. Dietrich
  • F. Di Paola
  • E. Galanti
  • L. Garrote
  • N. Harats
  • D. Katsanos
  • M. Kohn
  • V. Kotroni
  • M. Llasat-Botija
  • B. Lynn
  • L. Mediero
  • E. Morin
  • K. Nicolaides
  • S. Rozalis
  • K. Savvidou
  • B. Ziv
Article

Abstract

One of the costliest natural hazards around the globe is flash floods, resulting from localized intense convective precipitation over short periods of time. Since intense convective rainfall (especially over the continents) is well correlated with lightning activity in these storms, a European Union FP6 FLASH project was realized from 2006 to 2010, focusing on using lightning observations to better understand and predict convective storms that result in flash floods. As part of the project, 23 case studies of flash floods in the Mediterranean region were examined. For the analysis of these storms, lightning data were used together with rainfall estimates in order to understand the storms’ development and electrification processes. In addition, these case studies were simulated using mesoscale meteorological models to better understand the local and synoptic conditions leading to such intense and damaging storms. As part of this project, tools for short-term predictions (nowcasts) of intense convection across the Mediterranean and Europe, and long-term forecasts (a few days) of the likelihood of intense convection, were developed and employed. The project also focused on educational outreach through a special Web site http://flashproject.org supplying real-time lightning observations, real-time experimental nowcasts, medium-range weather forecasts and educational materials. While flash floods and intense thunderstorms cannot be prevented, long-range regional lightning networks can supply valuable data, in real time, for warning the public, end-users and stakeholders of imminent intense rainfall and possible flash floods.

Keywords

FLASH project Flash floods Lightning Thunderstorms 

References

  1. Adamo C, Goodman S, Mugnai A, Weinman JA (2009) Lightning measurements from satellites and significance for storms in the Mediterranean. In: Betz HD, Schumann U, Laroche P (eds) Lightning: principles, instruments and applications. Review of modern lightning research. Springer, Berlin, pp 309–329Google Scholar
  2. Alexander GD, Weinman JA, Karyampudi VM, Olson WS, Lee ACL (1999) The effect of assimilating rain rates derived from satellites, lightning on forecasts of the 1993 superstorm. Mon Weather Rev 127:1433–1457CrossRefGoogle Scholar
  3. Atencia A, Llasat MC, Garrote L, Mediero L (2010) Effect of radar rainfall time resolution on the predictive capability of a distributed hydrologic model. Hydrol Earth Syst Sci Discuss 7:7995–8043CrossRefGoogle Scholar
  4. Barnolas M, Llasat MC (2007) A flood geodatabase and its climatological applications: the case of Catalonia for the last century. Nat Haz Earth Syst Sci 7:271–281CrossRefGoogle Scholar
  5. Barredo JI (2007) Major flood disasters in Europe: 1950–2005. Nat Hazard 42(1):125–148. doi: 10.1007/s11069-006-9065-2 CrossRefGoogle Scholar
  6. Betz HD, Schmidt K, Oettinger WP, Montag B (2008) Cell-tracking with lightning data from LINET. Adv. Geosci. 17:55–61CrossRefGoogle Scholar
  7. Betz H-D, Schmidt K, Oettinger WP (2009) LINET—An international VLF/LF lightning detection network in Europe. In: Betz H-D, Schumann U, Laroche P (eds) Lightning: principles, instruments and applications, chap 5. Springer, DordrechtGoogle Scholar
  8. Carey LD, Petersen WA, Rutledge SA (2003) Evolution of cloud-to-ground lightning, storm structure in the Spencer, South Dakota, tornadic supercell of 30 May 1998. Mon Weather Rev 131:1811–1831CrossRefGoogle Scholar
  9. Carter AE, Kidder RE (1976) Lightning in relation to precipitation. J. Atmos. Terr. Phys. 39:139–148CrossRefGoogle Scholar
  10. Christian HJ et al (2003) Global frequency and distribution of lightning as observed from space by the optical transient detector. J Geophys Res 108(1D):4005. doi: 10.1029/2002JD002347 CrossRefGoogle Scholar
  11. Defer E, Lagouvardos K, Kotroni V (2005) Lightning activity in the eastern Mediterranean region. J Geophys Res 110:D24210. doi: 10.1029/2004JD005710 CrossRefGoogle Scholar
  12. Dietrich S, Casella D, Di Paola F, Formenton M, Mugnai A, Sanò P (2011) Lightning-based propagation of convective rain fields. Nat Hazards Earth Syst Sci 11:1571–1581CrossRefGoogle Scholar
  13. Dotzek N, Price C (2009) Lightning characteristics of extreme weather events. In: Betz HD, Schumann U, Laroche P (eds) Lightning: principles, instruments and applications. Springer, Berlin, pp 487–508Google Scholar
  14. Dowden RL, Holzworth RH, Rodger CJ, Lichtenberger J, Thomson NR, Jacobson AR, Lay E, Brundell JB, Lyons TJ, O’Keefe S, Kawasaki Z, Price C, Prior V, Ortéga P, Weinman J, Mikhailov Y, Veliz O, Qie X, Burns G, Collier A, Pinto Jr O, Diaz R, Adamo C, Williams ER, Kumar S, Raga GB, Rosado JM, Avila EE, Clilverd MA, Ulich T, Gorham P, Shanahan TJG, Osipowicz T, Cook G, Zhao Y (2008) World-wide lightning location using VLF propagation in the earth-ionosphere waveguide. IEEE Antennas Propag Mag 50(5):40–60Google Scholar
  15. Dudhia J (1989) Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci 46:3077–3107CrossRefGoogle Scholar
  16. Ezcurra A, Areitio J, Herrero I (2002) Relationship between CG lightning and surface rainfall during 1992–1996 in the Spanish Basque Country area. Atmos. Res. 61:239–250CrossRefGoogle Scholar
  17. Garrote L, Bras RL (1995a) A distributed model for real-time forecasting using digital elevation models. J Hydrol 167:279–306CrossRefGoogle Scholar
  18. Garrote L, Bras RL (1995b) An integrated software environment for real-time use of a distributed hydrologic model. J Hydrol 167:307–326CrossRefGoogle Scholar
  19. Geer IW (ed) (1996) Glossary of weather and climate. Am Meteorol Soc, Boston, 272 pGoogle Scholar
  20. Goodman SJ, Buechler DE, Wright PD, Rust WD (1988) Lightning and precipitation history of a microburst-producing storm. Geophys Res Lett 15(11):1185–1188CrossRefGoogle Scholar
  21. Gungle B, Krider EP (2006) Cloud-to-ground lightning and surface rainfall in warm-season Florida thunderstorms. J Geophys Res 111:D19203. doi: 10.1029/2005JD006802
  22. Harats N, Ziv B, YairY, Kotroni V, Dayan U (2010) Lightning and rain dynamic indices as predictors for flash floods events in the Mediterranean. Adv Geosci 23:57–64, doi: 10.5194/adgeo-23-57-2010
  23. Intergovernmental Panel on Climate Change (IPCC) (2007) Climate change 2007: the physical science basis. World Meteorological Organization (WMO) and UN Environment Programme (UNEP)Google Scholar
  24. ISDR (2002) Natural disasters and sustainable development: understanding the links between development, environment and natural disasters. United Nations international strategy for disaster reduction. United Nations Publications Centre, GenevaGoogle Scholar
  25. Kain JS, Fritsch JM (1993) The representation of cumulus convection in numerical models. Meteorol Monger 46:165–177Google Scholar
  26. Kohn M, Galanti E, Price C, Lagouvardos K, Kotroni V (2010) Now-casting thunderstorms in the Mediterranean region using lightning data. Atmos Res 100:489–502CrossRefGoogle Scholar
  27. Kotroni V, Lagouvardos K (2001) Precipitation forecast skill of different convective parameterization and microphysical schemes: application for the cold season over Greece. Geophys Res Lett 28(10):1977–1980. doi: 10.1029/2000GL012705 CrossRefGoogle Scholar
  28. Kotroni V, Lagouvardos K (2008) Lightning occurrence in relation with elevation, terrain slope and vegetation cover in the Mediterranean. J Geophys Res 113:D21118. doi: 10.1029/2008JD010605 CrossRefGoogle Scholar
  29. Lagouvardos K, Kotroni V, Betz H-D, Schmidt K (2009) A comparison of lightning data provided by ZEUS and LINET networks over Western Europe. Nat Hazard Earth Syst Sci 9:1713–1717CrossRefGoogle Scholar
  30. Llasat MC, Llasat-Botija M (2008) Climate change and the perception of the risk of flooding in Catalonia. In: Tàbara JD (ed) Public perception and policy of climate change in Catalonia. CADS, Barcelona, pp 55–69Google Scholar
  31. Llasat MC, Llasat-Botija M, López L (2009a) A press database on natural risks and its application in the study of floods in northeastern Spain. Nat Hazard Earth Syst Sci 9:2049–2061CrossRefGoogle Scholar
  32. Llasat MC, Llasat-Botija M, Barnolas M, López L, Altava-Ortiz V (2009b) An analysis of the evolution of hydrometeorological extremes in newspapers: the case of Catalonia, 1982–2006. Nat Hazard Earth Syst Sci 9:1201–1212CrossRefGoogle Scholar
  33. Llasat MC, Llasat-Botija M, Prat MA, Porcú F, Price C, Mugnai A, Lagouvardos K, Kotroni V, Katsanos D, Michaelides S, Yair Y, Savvidou K, Nicolaides K (2010a) High-impact floods and flash floods in Mediterranean countries: the FLASH preliminary database. Adv Geosci 23:47–55CrossRefGoogle Scholar
  34. Llasat MC, Llasat-Botija M, Rodriguez A, Lindbergh S (2010b) Flash floods in Catalonia: a recurrent situation. Adv Geosci 26:105–111CrossRefGoogle Scholar
  35. Lynn B, Yair Y (2010) Prediction of lightning flash density with the WRF model. Adv Geosci 23:11–16. doi: 10.5194/adgeo-23-11-2010 Google Scholar
  36. Mansell ER, Ziegler CL, MacGorman DR (2007) A lightning data assimilation technique for mesoscale forecast models. Mon Weather Rev 135:1732–1748CrossRefGoogle Scholar
  37. Marchi L, Borga M, Preciso E, Gaume E (2010) Characterisation of selected extreme flash floods in Europe and implications for flood risk management. J Hydrol 394(1–2):118–133. doi: 10.1016/j.jhydrol.2010.07.017) CrossRefGoogle Scholar
  38. Mediero L, Garrote L, Martín-Carrasco FJ (2011) Probabilistic calibration of a distributed hydrologic model for flood forecasting. Hydrol Sci J.  doi:10.1080/02626667.2011
  39. Morin E, Harats N, Jacoby Y, Arbel S, Getker M, Arazi A, Grodek T, Ziv B, Dayan U (2007) Studying the extremes: hydrometeorological investigation of a flood-causing rainstorm over Israel. Adv. Geosci. 12:107–114CrossRefGoogle Scholar
  40. Papadopoulos T, Chronis G, Anagnostou EN (2005) Improving convective precipitation forecasting through assimilation of regional lightning measurements in a mesoscale model. Mon Weather Rev 133:1961–1977CrossRefGoogle Scholar
  41. Pessi A, Businger S (2009) The impact of lightning data assimilation on a winter storm simulation over the North Pacific Ocean. Mon Wea Rev 137:3177–3195Google Scholar
  42. Piepgrass MV, Krider EP, Moore CB (1982) Lightning and surface rainfall during Florida thunderstorms. J Geophys Res 87(C13):11193–12001CrossRefGoogle Scholar
  43. Price C (2008) Lightning sensors for observing, tracking and nowcasting severe weather. Sensors 8:157–170CrossRefGoogle Scholar
  44. Price C, the FLASH team (2011) The FLASH project: using lightning data to better understand and forecast flash floods. Environ Sci Policy (in press).  doi:10.1016/j.envsci.2011.03.004
  45. Price C, Federmesser B (2006) Lightning-rainfall relationships in Mediterranean winter thunderstorms. Geophys Res Lett 33:L07813. doi: 10.1029/2005GL024794 CrossRefGoogle Scholar
  46. Price C, Murphy B (2002) Lightning activity during the 1999 Superior Derecho. Geophys Res Lett 29(23):57.1–57.4Google Scholar
  47. Price C, Asfur M, Yair Y (2009) Maximum hurricane intensity preceded by increase in lightning frequency. Nat Geosci 2:329–332. doi: 10.1038/NGEO477 CrossRefGoogle Scholar
  48. Proctor DE (1983) Lightning and precipitation in a small multicellular thunderstorm. J Geophys Res 88(C9):5421–5440CrossRefGoogle Scholar
  49. Rodger CJ, Werner S, Brundell JB, Lay EH, Thomson NR, Holzworth RH, Dowden RL (2006) Detection efficiency of the VLF World-Wide Lightning Location Network (WWLLN): initial case study. Ann Geophys 24:3197–3214CrossRefGoogle Scholar
  50. Rozalis S, Morin E, Yair Y, Price C (2010) Flash flood prediction using an uncalibrated hydrological model and radar rainfall data in a Mediterranean watershed under changing hydrological conditions. J Hydrol. 394:245–255Google Scholar
  51. Ruin I, Creutin JD, Anquetin S, Lutoff C (2008) Human exposure to flash-floods–relation between flood parameters and human vulnerability during a storm of September 2002 in Southern France. J Hydrol 361(1–2):199–213CrossRefGoogle Scholar
  52. Sanò P, Casella D, Mugnai A, Schiavon G, Smith EA, Tripoli GJ (2011) Bayesian estimation of precipitation from space-borne microwave radiometers using the Cloud Dynamics and Radiation Database (CDRD) approach: description and application to case studies over Italy. IEEE Trans Geosci Remote Sens (submitted)Google Scholar
  53. Schultz P (1995) An explicit cloud physics parameterization for operational numerical weather prediction. Mon Weather Rev 123:3313–3317CrossRefGoogle Scholar
  54. Soula S, Chauzy S (2001) Some aspects of the correlation between lightning and rain activities in thunderstorms. Atmos. Res. 56:355–373CrossRefGoogle Scholar
  55. Soula S, Savageot H, Molinie G, Mesnard F, Chauzy S (1998) The CG lightning activity of storm causing a flashflood. Geophys Res Lett 25(8):1181–1184CrossRefGoogle Scholar
  56. Soula S, Seity Y, Feral L, Sauvageot H (2004) Cloud-to-ground lightning activity in hail-bearing storms. J Geophys Res Atmos 109:D02101. doi: 10.1029/2003JD003669
  57. Surussavadee C, Staelin DH (2008a) Global millimeter-wave precipitation retrievals trained with a cloud-resolving numerical weather prediction model. Part II: performance evaluation. IEEE Trans Geosci Remote Sens 46:109–118CrossRefGoogle Scholar
  58. Surussavadee C, Staelin DH (2008b) Global millimeter-wave precipitation retrievals trained with a cloud-resolving numerical weather prediction model. Part I: retrieval design. IEEE Trans Geosci Remote Sens 46:99–108CrossRefGoogle Scholar
  59. Tapia A, Smith J, Dixon M (1998) Estimation of convective rainfall from lightning observations. J Appl Meteorol 37:1497–1509CrossRefGoogle Scholar
  60. UN International Strategy for Disaster Reduction, UN–ISDR, 2005: Hyogo framework for action 2005–2015: Building the resilience of nations and community to disasters, Geneva. http://www.unisdr.org/eng/hfa/hfa.html
  61. Volland H (ed) (1995) Handbook of atmospheric electrodynamics. CRC Press, Boca RatonGoogle Scholar
  62. Yair Y, Lynn B, Price C, Kotroni V, Lagouvardos K, Morin E, Mugnai A, Llasat MC (2009) Predicting lightning density in Mediterranean storms based on the WRF model dynamic and microphysical fields. J Geophys Res 115:D04205. doi: 10.1029/2008JD010868 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • C. Price
    • 1
  • Y. Yair
    • 2
  • A. Mugnai
    • 3
  • K. Lagouvardos
    • 4
  • M. C. Llasat
    • 5
  • S. Michaelides
    • 6
  • U. Dayan
    • 7
  • S. Dietrich
    • 3
  • F. Di Paola
    • 3
  • E. Galanti
    • 1
  • L. Garrote
    • 8
  • N. Harats
    • 7
  • D. Katsanos
    • 4
  • M. Kohn
    • 1
  • V. Kotroni
    • 4
  • M. Llasat-Botija
    • 5
  • B. Lynn
    • 7
    • 2
  • L. Mediero
    • 8
  • E. Morin
    • 7
  • K. Nicolaides
    • 6
  • S. Rozalis
    • 1
  • K. Savvidou
    • 6
  • B. Ziv
    • 2
  1. 1.Tel Aviv UniversityTel AvivIsrael
  2. 2.The Open University of IsraelRa’ananaIsrael
  3. 3.National Research Council, ISACRomeItaly
  4. 4.National Observatory of AthensAthensGreece
  5. 5.University of BarcelonaBarcelonaSpain
  6. 6.Cyprus Meteorological ServiceNicosiaCyprus
  7. 7.Hebrew University of JerusalemJerusalemIsrael
  8. 8.Technical University of MadridMadridSpain

Personalised recommendations