Pure and Applied Geophysics

, Volume 175, Issue 4, pp 1445–1471 | Cite as

Evaluating the Effectiveness of DART® Buoy Networks Based on Forecast Accuracy

  • Donald B. Percival
  • Donald W. Denbo
  • Edison Gica
  • Paul Y. Huang
  • Harold O. Mofjeld
  • Michael C. Spillane
  • Vasily V. Titov


A performance measure for a DART® tsunami buoy network has been developed. DART® buoys are used to detect tsunamis, but the full potential of the data they collect is realized through accurate forecasts of inundations caused by the tsunamis. The performance measure assesses how well the network achieves its full potential through a statistical analysis of simulated forecasts of wave amplitudes outside an impact site and a consideration of how much the forecasts are degraded in accuracy when one or more buoys are inoperative. The analysis uses simulated tsunami amplitude time series collected at each buoy from selected source segments in the Short-term Inundation Forecast for Tsunamis database and involves a set for 1000 forecasts for each buoy/segment pair at sites just offshore of selected impact communities. Random error-producing scatter in the time series is induced by uncertainties in the source location, addition of real oceanic noise, and imperfect tidal removal. Comparison with an error-free standard leads to root-mean-square errors (RMSEs) for DART® buoys located near a subduction zone. The RMSEs indicate which buoy provides the best forecast (lowest RMSE) for sections of the zone, under a warning-time constraint for the forecasts of 3 h. The analysis also shows how the forecasts are degraded (larger minimum RMSE among the remaining buoys) when one or more buoys become inoperative. The RMSEs provide a way to assess array augmentation or redesign such as moving buoys to more optimal locations. Examples are shown for buoys off the Aleutian Islands and off the West Coast of South America for impact sites at Hilo HI and along the US West Coast (Crescent City CA and Port San Luis CA, USA). A simple measure (coded green, yellow or red) of the current status of the network’s ability to deliver accurate forecasts is proposed to flag the urgency of buoy repair.


Aleutian Islands tsunami sources Buoy network performance measure Crescent City CA DART® data inversion Hilo HI Network assessment Port San Luis CA South American tsunami sources Tsunameter Tsunami buoys Tsunami forecasts Tsunami simulation Tsunami source estimation 



This work was funded by the Joint Institute for the Study of the Atmosphere and Ocean (JISAO) under NOAA Cooperative Agreement No. NA15OAR4320063 and is JISAO Contribution No. 2714. This work is also Contribution No. 4507 from NOAA/Pacific Marine Environmental Laboratory. The authors thank Peter Dahl for discussion on a running example.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Applied Physics LaboratoryUniversity of WashingtonSeattleUSA
  2. 2.Department of StatisticsUniversity of WashingtonSeattleUSA
  3. 3.NOAA/Pacific Marine Environmental LaboratorySeattleUSA
  4. 4.Joint Institute for the Study of the Atmosphere and OceanUniversity of WashingtonSeattleUSA
  5. 5.National Tsunami Warning CenterNational Weather ServicePalmerUSA

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