Forecast Variance Estimates Using Dart Inversion

Abstract:

The tsunami forecast tool developed by the NOAA Center for Tsunami Research (NCTR) provides real-time tsunami forecast and is composed of the following major components: a pre-computed tsunami propagation database, an inversion algorithm that utilizes real-time tsunami data recorded at DART stations to define the tsunami source, and inundation models that predict tsunami wave characteristics at specific coastal locations.

The propagation database is a collection of basin-wide tsunami model runs generated from 50x100 km “unit sources” with a slip of 1 meter. Linear combination and scaling of unit sources is possible since the nonlinearity in the deep ocean is negligible. To define the tsunami source using the unit sources, real-time DART data is ingested into an inversion algorithm. Based on the selected DART and length of tsunami time series, the inversion algorithm will select the best combination of unit sources and scaling factors that best fit the observed data at the selected locations. This combined source then serves as boundary condition for the inundation models.

Different combinations of DARTs and length of tsunami time series used in the inversion algorithm will result in different selection of unit sources and scaling factors. Since the combined unit sources are used as boundary condition for inundation modeling, different sources will produce variations in the tsunami wave characteristics. As part of the testing procedures for the tsunami forecast tool, staff at NCTR and both National and Pacific Tsunami Warning Centers, performed post-event forecasts for several historical tsunamis. The extent of variation due to different source definitions obtained from the testing is analyzed by comparing the simulated maximum tsunami wave amplitude with recorded data at tide gauge locations. Results of the analysis will provide an error estimate defining the possible range of the simulated maximum tsunami wave amplitude for each specific inundation model.