Tsunami Wavefield Reconstruction and Forecasting Using the Ensemble Kalman Filter

Abstract

Offshore sensor networks like DONET and S-NET, providing real-time estimates of wave height through measurements of pressure changes along the seafloor, are revolutionizing local tsunami early warning. Data assimilation techniques, in particular, optimal interpolation (OI), provide real-time wavefield reconstructions and forecasts. Here we explore an alternative assimilation method, the ensemble Kalman filter (EnKF), and compare it to OI. The methods are tested on a scenario tsunami in the Cascadia subduction zone, obtained from a 2-D coupled dynamic earthquake and tsunami simulation. Data assimilation uses a 1-D linear long-wave model. We find that EnKF achieves more accurate and stable forecasts than OI, both at the coast and across the entire domain, especially for large station spacing. Although EnKF is more computationally expensive than OI, with development in high-performance computing, it is a promising candidate for real-time local tsunami early warning.