FY 2015 A Tsunami Forecast Model for Newport, Oregon Eblé, M., and NCTR Staff NOAA OAR Special Report, PMEL Tsunami Forecast Series: Vol. 5, 2nd Ed., 146 pp, doi: 10.7289/V5125QK9 (2014) The National Oceanic and Atmospheric Administration has developed a tsunami forecast model for Newport, Oregon, as part of an effort to provide tsunami forecasts for United States coastal communities. The forecast model is a set of nested grids constructed by incrementally subsampling and smoothing a reference high-resolution digital elevation model. During this process, forecast model results were monitored for deviations from those computed with the more accurate, higher-resolution model. Validation and stability testing of the tsunami forecast model developed for this economically important and populous coastal community was conducted to ensure model performance and robustness across a suite of scenarios. A total of 11 historical tsunami events and 19 synthetically generated mega-tsunami (Mw 9.3) events around the Pacific basin were used for validation and stability testing of the Newport forecast model. Validation results show that model output track observed data within an expected accuracy tolerance, thus providing a quantitative estimate of the tsunami time series, inundation, and runup at Newport for tested events. In addition to robustness, reproducibility of results was verified by comparing 2009 development results with those attained during end-to-end operational system testing performed in 2013. The differences noted in this report are attributed to the Method of Splitting Tsunami numerical code updates made after the Newport model was developed (2004) and updated (2009). Of greatest significance, 2013 test results more accurately compare with observations during the 2011 Tohoku tsunami than the earlier development results. Overall, validation results combined with benchmarking show that the forecast model developed for Newport consistently generates 4 hr of tsunami simulation in significantly less than 10 min of CPU time without compromising forecast results for all scenarios tested. Feature Publications | Outstanding Scientific Publications Contact Sandra Bigley | Help
