Pure and Applied Geophysics

, Volume 174, Issue 8, pp 3249–3273 | Cite as

A Response Function Approach for Rapid Far-Field Tsunami Forecasting

  • Elena TolkovaEmail author
  • Dmitry Nicolsky
  • Dailin Wang


Predicting tsunami impacts at remote coasts largely relies on tsunami en-route measurements in an open ocean. In this work, these measurements are used to generate instant tsunami predictions in deep water and near the coast. The predictions are generated as a response or a combination of responses to one or more tsunameters, with each response obtained as a convolution of real-time tsunameter measurements and a pre-computed pulse response function (PRF). Practical implementation of this method requires tables of PRFs in a 3D parameter space: earthquake location–tsunameter–forecasted site. Examples of hindcasting the 2010 Chilean and the 2011 Tohoku-Oki tsunamis along the US West Coast and beyond demonstrated high accuracy of the suggested technology in application to trans-Pacific seismically generated tsunamis.


Tsunami forecast DART station pulse response function source inversion boundary value problem 



We acknowledge NOAA/NDBC for providing the DART records, NOAA/NOS for providing the tide gauge records, Ocean Networks Canada for providing the NEPTUNE records, and NOAA/NCEI for providing the bathymetry used in the numerical simulations. Dmitry Nicolsky acknowledges support for his work from the state of Alaska.

Supplementary material

24_2017_1612_MOESM1_ESM.pdf (1.5 mb)
Supplementary material 1 (pdf 1553 kb)


  1. Burwell, D., Tolkova, E., & Chawla, A. (2007). Diffusion and dispersion characterization of a numerical tsunami model. Ocean Modelling, 19, 10–30.CrossRefGoogle Scholar
  2. Butler, R., Howe, B. M., & Science and Society Committee, J. (2014). ‘Green’ submarine telecommunication cables to monitor global change and tsunamis in the deep ocean. Abstract ID:17514. Ocean Sciences Meeting, 23–28 Feb. 2014, Honolulu, Hawaii USA.Google Scholar
  3. Catalan, P. A., Aranguiz, R., Gonzalez, G., Tomita, T., Cienfuegos, R., Gonzalez, J., et al. (2015). The 1 April 2014 Pisagua tsunami: Observations and modeling. Geophysical Research Letters, 42, 2918–2925. doi: 10.1002/2015GL063333.CrossRefGoogle Scholar
  4. Crowell, B. W., Bock, Y., & Melgar, D. (2012). Real-time inversion of GPS data for finite fault modeling and rapid hazard assessment. Geophysical Research Letters, 39, L09305. doi: 10.1029/2012GL051318.CrossRefGoogle Scholar
  5. Foster, J. H., Brooks, B. A., Wang, D., Carter, G. S., & Merrifield, M. A. (2012). Improving tsunami warning using commercial ships. Geophysical Research Letters, 39, L09603. doi: 10.1029/2012GL051367.CrossRefGoogle Scholar
  6. Geist, E. L. (2013). Near-field tsunami edge waves and complex earthquake rupture. Pure and Applied Geophysics, 170(9), 1475–1491. doi: 10.1007/s00024-012-0491-7.CrossRefGoogle Scholar
  7. Gica, E., Spillane, M. C., Titov, V. V., Chamberlin, C. D., & Newman, J. C. (2008). Development of the forecast propagation database for NOAA’s Short-Term Inundation Forecast for Tsunamis (SIFT). NOAA Tech. Memo. OAR PMEL-139.Google Scholar
  8. Imamura, F. (1996). Review of tsunami simulation with a finite difference method. In H. Yeh, P. Liu, & C. Synolakis (Eds.), Long-wave runup models (pp. 25–42). Singapore: World Scientific.Google Scholar
  9. Maeda, T., Obara, K., Shinohara, M., Kanazawa, T., & Uehira, K. (2015). Successive estimation of a tsunami wavefield without earthquake source data: A data assimilation approach toward real-time tsunami forecasting. Geophysical Research Letters, 42, 7923–7932. doi: 10.1002/2015GL065588.CrossRefGoogle Scholar
  10. Melgar, D., Crowell, B. W., Bock, Y., & Haase, J. S. (2013). Rapid modeling of the 2011 Mw 9.0 Tohoku-oki earthquake with seismogeodesy. Geophysical Research Letters, 40, 2963–2968. doi: 10.1002/grl.50590.CrossRefGoogle Scholar
  11. Melgar, D., et al. (2016). Local tsunami warnings: Perspectives from recent large events. Geophysical Research Letters, 43(3), 1109–1117. doi: 10.1002/2015GL067100.CrossRefGoogle Scholar
  12. Nicolsky, D. J., Suleimani, E. N., & Hansen, R. A. (2011). Validation and verification of a numerical model for tsunami propagation and runup. Pure and Applied Geophysics, 168, 1199–1222. doi: 10.1007/s00024-010-0231-9.CrossRefGoogle Scholar
  13. Nicolsky, D. J., Suleimani, E. N., Freymueller, J. T., & Koehler, R. D. (2015). Tsunami inundation maps of Fox Islands communities, including Dutch Harbor and Akutan, Alaska: Alaska Division of Geological & Geophysical Surveys Report of Investigation 2015-5, 67 p., 2 sheets, scale 1:12,500. doi: 10.14509/29414
  14. Percival, D. B., Denbo, D. W., Eble, M. C., Gica, E., Mofjeld, H. O., Spillane, M. C., et al. (2011). Extraction of tsunami source coefficients via inversion of DART buoy data. Natural Hazards, 58(1), 567–590. doi: 10.1007/s11069-010-9688-1.CrossRefGoogle Scholar
  15. Power, W., & Tolkova, E. (2013). Forecasting tsunamis in Poverty Bay, New Zealand, with deep-ocean gauges. Ocean Dynamics, 63(11), 1213–1232. doi: 10.1007/s10236-013-0665-6.CrossRefGoogle Scholar
  16. Suleimani, E. N., Nicolsky, D. J., & Koehler, R. D. (2013). Tsunami inundation maps of Sitka, Alaska: Alaska Division of Geological & Geophysical Surveys Report of Investigation 2013-3, 76 p., 1 sheet, scale 1:250,000. doi: 10.14509/26671
  17. Sweldens, W., & Schrder, P. (2000). Building your own wavelets at home. Lecture Notes in Earth Sciences, 90(2000), 72–107. doi: 10.1007/BFb0011093.CrossRefGoogle Scholar
  18. Tang, L., Titov, V. V., Wei, Y., Mofjeld, H. O., Spillane, M., Arcas, D., et al. (2008). Tsunami forecast analysis for the May 2006 Tonga tsunami. Journal of Geophysical Research, 113, C12015. doi: 10.1029/2008JC004922.CrossRefGoogle Scholar
  19. Tang, L., Titov, V. V., Bernard, E., Wei, Y., Chamberlin, C., Newman, J. C., et al. (2012). Direct energy estimation of the 2011 Japan tsunami using deep-ocean pressure measurements. Journal of Geophysical Research, 117, C08008. doi: 10.1029/2011JC007635.CrossRefGoogle Scholar
  20. Tang, L., Titov, V. V., Moore, C., & Wei, Y. (2016). Real-time assessment of the 16 September 2015 Chile Tsunami and implications for near-field forecast. Pure and Applied Geophysics, 173, 369–387. doi: 10.1007/s00024-015-1226-3.CrossRefGoogle Scholar
  21. Thomson, R., Fine, I., Rabinovich, A., Mihaly, S., Davis, E., Heesemann, M., et al. (2011). Observation of the 2009 Samoa tsunami by the NEPTUNE—Canada cabled observatory: Test data for an operational regional tsunami forecast model. Geophysical Research Letters, 38, L11701. doi: 10.1029/2011GL046728.CrossRefGoogle Scholar
  22. Titov, V. V., Mofjeld, H. O., Gonzalez, F. I., & Newman, J. C. (1999). Offshore forecasting of Hawaiian tsunamis generated in Alaska-Aleutian Subduction Zone. NOAA Tech. Memo. ERL PMEL-114, NTIS: PB2002-101567, NOAA/Pacific Marine Environmental Laboratory, Seattle, WA.Google Scholar
  23. Titov, V., Kanoglu, U., Synolakis, C. (2016). Development of MOST for real-time tsunami forecasting. Journal of Waterway, Port, Coastal and Ocean Engineering. doi: 10.1061/(ASCE) WW.1943-5460.0000357 (on-line first)
  24. Titov, V., Song, T., Tang, L., Bernard, E. N., Bar-Sever, Y., & Wei, Y. (2016). Consistent estimates of tsunami energy show promise for improved early warning. Pure and Applied Geophysics, 173, 3863-3880. doi: 10.1007/s00024-016-1312-1.
  25. Tolkova, E. (2014). Land-water boundary treatment for a tsunami model with dimensional splitting. Pure and Applied Geophysics, 171(9), 2289–2314. doi: 10.1007/s00024-014-0825-8.CrossRefGoogle Scholar
  26. Tolkova, E. (2016). Cliffs benchmarking. arXiv:1601.06486
  27. Tsai, V. C., Ampuero, J. P., Kanamori, H., & Stevenson, D. J. (2013). Estimating the effect of Earth elasticity and variable water density on tsunami speeds. Geophysical Research Letters, 40, 492–496. doi: 10.1002/grl.50147.CrossRefGoogle Scholar
  28. Wang, D., Becker, N. C., Walsh, D., Fryer, G. J., Weinstein, S. A., McCreery, C. S., et al. (2012). Real-time forecasting of the April 11, 2012 Sumatra tsunami. Geophysical Research Letters, 39, L19601. doi: 10.1029/2012GL053081.Google Scholar
  29. Wang, D. (2015). An ocean depth-correction method for reducing model errors in tsunami travel time: Application to the 2010 Chile and 2011 Tohoku tsunamis. Science of Tsunami Hazards, 34(1), 1–22.Google Scholar
  30. Watada, S., Kusumoto, S., & Satake, K. (2014). Traveltime delay and initial phase reversal of distant tsunamis coupled with the self-gravitating elastic Earth. Journal of Geophysical Research: Solid Earth, 119, 4287–4310. doi: 10.1002/2013JB010841.Google Scholar
  31. Wei, Y., Bernard, E. N., Tang, L., Weiss, R., Titov, V. V., Moore, C., et al. (2008). Real-time experimental forecast of the Peruvian tsunami of August 2007 for US coastlines. Geophysical Research Letters, 35, L04609. doi: 10.1029/2007GL032250.Google Scholar
  32. Wilson, R. I., Admire, A. R., Borrero, J. C., Dengler, L. A., Legg, M. R., Lynett, P., et al. (2013). Observations and impacts from the 2010 Chilean and 2011 Japanese tsunamis in California (USA). Pure and Applied Geophysics, 170(6), 1127–1147. doi: 10.1007/s00024-012-0527-z.CrossRefGoogle Scholar
  33. Xing, X., Kou, Z., Huang, Z., & Lee, J.-J. (2013). Frequency domain response at Pacific coast harbors to major tsunamis of 2005–2011. Pure and Applied Geophysics, 170(6), 1149–1168. doi: 10.1007/s00024-012-0526-0.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.NorthWest Research AssociatesBellevueUSA
  2. 2.University of Alaska FairbanksFairbanksUSA
  3. 3.NOAA/NWS/Pacific Tsunami Warning CenterHonoluluUSA

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