Natural Hazards

, Volume 74, Issue 1, pp 123–142 | Cite as

A framework for the probabilistic analysis of meteotsunamis

Original Paper

Abstract

A probabilistic technique is developed to assess the hazard from meteotsunamis. Meteotsunamis are unusual sea-level events, generated when the speed of an atmospheric pressure or wind disturbance is comparable to the phase speed of long waves in the ocean. A general aggregation equation is proposed for the probabilistic analysis, based on previous frameworks established for both tsunamis and storm surges, incorporating different sources and source parameters of meteotsunamis. Parameterization of atmospheric disturbances and numerical modeling is performed for the computation of maximum meteotsunami wave amplitudes near the coast. A historical record of pressure disturbances is used to establish a continuous analytic distribution of each parameter as well as the overall Poisson rate of occurrence. A demonstration study is presented for the northeast U.S. in which only isolated atmospheric pressure disturbances from squall lines and derechos are considered. For this study, Automated Surface Observing System stations are used to determine the historical parameters of squall lines from 2000 to 2013. The probabilistic equations are implemented using a Monte Carlo scheme, where a synthetic catalog of squall lines is compiled by sampling the parameter distributions. For each entry in the catalog, ocean wave amplitudes are computed using a numerical hydrodynamic model. Aggregation of the results from the Monte Carlo scheme results in a meteotsunami hazard curve that plots the annualized rate of exceedance with respect to maximum event amplitude for a particular location along the coast. Results from using multiple synthetic catalogs, resampled from the parent parameter distributions, yield mean and quantile hazard curves. Further refinements and improvements for probabilistic analysis of meteotsunamis are discussed.

Keywords

Meteotsunami Probabilistic analysis Squall line Derecho Shallow-water wave Linear long wave 

References

  1. Aida I (1969) Numerical experiments for the tsunami propagation—the 1964 Niigata tsunami and the 1968 Tokachi-Oki tsunami. Bull Earthq Res Inst 47:673–700Google Scholar
  2. Annaka T, Satake K, Sakakiyama T, Yanagisawa K, Shuto N (2007) Logic-tree approach for probabilistic tsunami hazard analysis and it applications to the Japanese Coasts. Pure Appl Geophys 164:577–592CrossRefGoogle Scholar
  3. Asano T, Yamashiro T, Nishimura N (2012) Field observations of meteotsunami locally called “abiki” in Urauchi Bay, Kami-Koshiki Island, Japan. Nat Hazards 64:1685–1706CrossRefGoogle Scholar
  4. Bluestein HB (1993) In synoptic–dynamic meteorology in midlatitudes: observations and theory of weather systems, vol 2. Oxford University Press, Oxford, p 594Google Scholar
  5. Box GEP, Muller ME (1958) A note on the generation of random normal deviates. Ann Math Stat 29:610–611CrossRefGoogle Scholar
  6. Carrier GF (1995) On-shelf tsunami generation and coastal propagation. In: Tsuchiya Y, Shuto N (eds) Tsunami: progress in prediction, disaster prevention and warning, vol 4. Kluwer, Dordrecht, pp 1–20Google Scholar
  7. Churchill DD, Houston SH, Bond NA (1995) The Daytona Beach wave of 3–4 July 1992: a shallow-water gravity wave forced by a propagating squall line. Bull Am Meteorol Soc 76:21–32CrossRefGoogle Scholar
  8. Fujima K, Dozono R, Shigemura T (2000) Generation and propagation of tsunami accompanying edge waves on a uniform shelf. Coast Eng J 42:211–236Google Scholar
  9. Geist EL (2012) Near-field tsunami edge waves and complex earthquake rupture. Pure Appl Geophys. doi:10.1007/s00024-012-0491-7 Google Scholar
  10. Geist EL, Oglesby DD (2013) Earthquake mechanics and tsunami generation. In: Beer M, Patelli E, Kougioumtzoglou I, Au IS-K (eds) Encyclopedia of Earthquake Engineering, Springer, New YorkGoogle Scholar
  11. Geist EL, Parsons T (2006) Probabilistic analysis of tsunami hazards. Nat Hazards 37:277–314CrossRefGoogle Scholar
  12. Geist EL, Parsons T (2014) Undersampling power-law size distributions: effect on the assessment of extreme natural hazards. Nat Hazards 72:565–595CrossRefGoogle Scholar
  13. Geist EL, Parsons T, ten Brink US, Lee HJ (2009) Tsunami probability. In: Bernard EN, Robinson AR (eds) The sea, vol 15. Harvard University Press, Cambridge, pp 93–135Google Scholar
  14. Greenspan HP (1956) The generation of edge waves by moving pressure distributions. J Fluid Mech 1:574–592CrossRefGoogle Scholar
  15. Hibiya T, Kajiura K (1982) Origin of the Abiki phenomenon (a kind of seiche) in Magasaki Bay. J Oceanogr Soc Jpn 38:172–182CrossRefGoogle Scholar
  16. Horvath K, Vilibić I (2014) Atmospheric mesoscale conditions during the Boothbay meteotsunami: a numerical sensitivity study using a high-resolution mesoscale model. Nat Hazards. doi:10.1007/s11069-014-1055-1 Google Scholar
  17. Irish JL, Resio DT, Cialone MA (2009) A surge response function approach to coastal hazard assessment. Part 2: quantification of spatial attributes of response functions. Nat Hazards 51:183–205CrossRefGoogle Scholar
  18. Irish JL, Song YK, and Chang K-A (2011) Probabilistic hurricane surge forecasting using parameterized surge response functions. Geophys Res Lett 38. doi:10.1029/2010GL046347
  19. Lamb H (1932) In hydrodynamics, vol 6. Dover Publications, Mineola, p 768Google Scholar
  20. Lynett PJ, Liu PL-F (2005) A numerical study of run-up generated by three-dimensional landslides. J Geophys Res 10. doi:10.1029/2004JC002443
  21. Martinsen EA, Gjevik B, Röed LP (1979) A numerical model for long barotropic waves and storm surges along the western coast of Norway. J Phys Oceanogr 9:1126–1138CrossRefGoogle Scholar
  22. Mercer D, Sheng J, Greatbatch RJ, Bobanović J (2002) Barotropic waves generated by storms moving rapidly over shallow water. J Geophys Res 107. doi:10.1029/2001JC001140
  23. Monserrat S, Ibbetson A, Thorpe AJ (1991) Atmospheric gravity waves and the “Rissaga” phenomenon. Q J R Meteorol Soc 117:553–570Google Scholar
  24. Monserrat S, Vilibić I, Rabinovich AB (2006) Meteotsunamis: atmospherically induced destructive ocean waves in the tsunami frequency band. Nat Hazards Earth Syst Sci 6:1035–1051CrossRefGoogle Scholar
  25. Parsons T, Geist EL (2009) Tsunami probability in the Caribbean region. Pure appl Geophys 165:2089–2116CrossRefGoogle Scholar
  26. Pasquet S, Vilibić I (2013) Shelf edge reflection of atmospherically generated long ocean waves alon gthe central U.S. East Coast. Cont Shelf Res 66:1–8CrossRefGoogle Scholar
  27. Press WH, Teukolsky SA, Vetterling WT, Flannery BP (2007) In numerical recipes: the art of scientific computing, vol 3. Cambridge University Press, Cambridge, p 1256Google Scholar
  28. Proudman J (1929) The effects on the sea of changes in atmospheric pressure. Geophys J Int 2:197–209CrossRefGoogle Scholar
  29. Pugh DT, Vassie JM (1978) Extreme sea levels from tide and surge probability. In: coastal engineering Proceedings, 16th international conference on Coastal Engineering, Hamburg, pp 911–30Google Scholar
  30. Rabinovich AB (2012) Meteorological tsunamis along the east coast of the United States. In: Towards a meteotsunami warning system along the U.S. Coastline (TMEWS), Institute of Oceanography and Fisheries, Croatia, 7 pp. http://jadran.izor.hr/tmews/results/Report-Rabinovich-TGdata-catalogues-analysis.pdf
  31. Rabinovich AB, Monserrat S (1996) Meteorological tsunamis near the Balearic and Kuril islands: descriptive and statistical analysis. Nat Hazards 13:55–90CrossRefGoogle Scholar
  32. Rabinovich AB, Monserrat S (1998) Generation of meteorological tsunamis (large amplitude seiches) near the Balearic and Kuril Islands. Nat Hazards 18:27–55CrossRefGoogle Scholar
  33. Rabinovich AB, Shevchenko GV, Sokolova SE (1992) An estimation of extreme sea levels in the northern part of the Sea of Japan. La Mer 30:179–190Google Scholar
  34. Raichlen F, Lee JJ (1992) Oscillation of bays, harbors, and lakes. In: Herbich JB (ed) Handbook of coastal and ocean engineering, vol 3. Gulf Publishing Company, Houston, pp 1073–1113Google Scholar
  35. Reid RO, Bodine BR (1968) Numerical model for storm surges in Galveston Bay. J Waterw Harb Division A.C.E. 94:33–57Google Scholar
  36. Renault L, Vizoso G, Jansá A, Wilkin J, Tintoré J (2011) Toward the predictability of meteotsunamis in the Balearic Sea using regional nested atmosphere and ocean models. Geophys Res Lett 38. doi:10.1029/2011GL047361
  37. Resio DT, Irish J, Cialone M (2009) A surge response function approach to coastal hazard assessment—part 1: basic concepts. Nat Hazards 51:163–182CrossRefGoogle Scholar
  38. Rotunno R, Klemp JB, Weisman ML (1988) A theory for strong, long-lived squall lines. J Atmos Sci 45:463–485CrossRefGoogle Scholar
  39. Sallenger AH, List JH, Gelfenbaum G, Stumpf RP, Hansen M (1995) Large wave at Daytona Beach, Florida, explained as a squall-line surge. J Coastal Res 11:1383–1388Google Scholar
  40. Salvadori G, De Michele C (2004) Frequency analysis via copulas: theoretical aspects and applications to hydrological events. Water Resour Res 40. doi:10.1029/2004WR003133
  41. Satake K (2007) Tsunamis. In: Kanamori H, Schubert G (eds) Treatise on geophysics, volume 4-earthquake seismology, vol 4. Elsevier, Amsterdam, pp 483–511CrossRefGoogle Scholar
  42. Tebaldi C, Strauss BH, and Zervas CE (2012) Modelling sea level rise impacts on storm surges along US coasts. Environ Res Lett 7. doi:10.1088/748-9326/7/1/014032
  43. ten Brink US, Chaytor JD, Geist EL, Brothers DS, Andrews BD (2014) Assessment of tsunami hazard to the U.S. Atlantic margin. Mar Geol 353:31–54CrossRefGoogle Scholar
  44. Toro GR, Resio DT, Divoky D, Niedoroda AW, Reed C (2010) Efficient joint-probability methods for hurrican surge frequency analysis. Ocean Eng 37:125–134CrossRefGoogle Scholar
  45. U.S. Nuclear Regulatory Commission (2013) In: Nicholson TJ, Reed W (eds) Proceedings of the workshop on probabilistic flood hazard assessment (PFHA). NUREG/CP-0302, RockvilleGoogle Scholar
  46. Vennell R (2007) Long barotropic waves generated by a storm crossing topography. J Phys Oceanogr 37:2809–2823CrossRefGoogle Scholar
  47. Vennell R (2010) Resonance and trapping of topographic transient ocean waves generated by a moving atmospheric disturbance. J Fluid Mech 650:427–443CrossRefGoogle Scholar
  48. Vickery PJ, Skerlj PF, Twisdale LA (2000) Simulation of hurricane risk in the U.S. using empirical track model. J Struct Eng 126:1222–1237CrossRefGoogle Scholar
  49. Vilibić I (2005) Numerical study of the Middle Adriatic coastal waters’ sensitivity to the various air pressure travelling disturbances. Ann Geophys 23:3569–3578CrossRefGoogle Scholar
  50. Vilibić I (2008) Numerical simulations of the Proudman resonance. Cont Shelf Res 28:574–581CrossRefGoogle Scholar
  51. Vilibić I, Domijan N, Orlić M, Leder N, and Pasarić M (2004) Resonant coupling of a traveling air pressure disturbance with the east Adriatic coastal waters. J Geophys Res 109. doi:10.1029/2004JC002279
  52. Vilibić I, Domijan N, Čupić S (2005) Wind versus air pressure seiche triggering in the Middle Adriatic coastal waters. J Mar Syst 57:189–200CrossRefGoogle Scholar
  53. Vilibić I, Monserrat S, Rabinovich AB, Mihanović H (2008) Numerical modelling of the destructive meteotsunami of 15 June, 2006 on the coast of the Balearic Islands. Pure appl Geophys 165:2169–2195CrossRefGoogle Scholar
  54. Vilibić I, Horvath K, Strelec Mohović N, Monserrat S, Marcos M, Amores Á et al (2013) Atmospheric processes responsible for generation of the 2008 Boothbay meteotsunami. Nat Hazards. doi:10.1007/s10069-013-0811-y Google Scholar

Copyright information

© US Government 2014

Authors and Affiliations

  • Eric L. Geist
    • 1
  • Uri S. ten Brink
    • 2
  • Matthew Gove
    • 3
  1. 1.U.S. Geological SurveyMenlo ParkUSA
  2. 2.U.S. Geological SurveyWoods HoleUSA
  3. 3.Oklahoma CityUSA

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