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On 22 December 2018, a devastating tsunami struck Sunda Strait, Indonesia without warning, leaving 437 dead and thousands injured along the western Java and southern Sumatra coastlines. Synthetic aperture radar and broadband seismic observations demonstrate that a small, <~0.2 km 3 landslide on the southwestern flank of the actively erupting volcano Anak Krakatau generated the tsunami. The landslide did not produce strong short-period seismic waves; thus, precursory ground shaking did not provide a tsunami warning. The source of long-period ground motions during the landslide can be represented as a 12° upward-dipping single-force directed northeastward, with peak magnitude of ~6.1 × 10 11 N and quasi-sinusoidal time duration of ~70 s. Rapid quantification of a landslide source process by long-period seismic wave inversions for moment-tensor and single-force parameterizations using regional seismic data available within ~8 min can provide a basis for future fast tsunami warnings, as is also the case for tsunami earthquakes. 

We use tsunami waveforms recorded on deep water absolute pressure gauges (Deep‐ocean Assessment and Reporting of Tsunamis), coastal tide gauges, and a temporary array of seafloor differential pressure gauges (DPG) to study the tsunami generated by the 15 July 2009 magnitude 7.8 Dusky Sound, New Zealand, earthquake. We first use tsunami waveform inversion applied to Deep‐ocean Assessment and Reporting of Tsunamis seafloor pressure gauge and coastal tide gauge data to estimate the fault slip distribution of the Dusky Sound earthquake. This fault slip estimate is then used to generate synthetic tsunami waveforms at each of the DPG sites. DPG instruments are unfortunately not well calibrated, but comparison of the synthetic tsunami waveforms to those observed at each DPG site allows us to determine an appropriate amplitude scaling to apply. We next use progressive data assimilation of the amplitude‐scaled DPG observations to retrospectively forecast the Dusky Sound tsunami wavefields and find a good match between forecast and observed tsunami wavefields at the Charleston tide gauge station on the west coast of New Zealand's South Island. While an advantage of the data assimilation method is that no initial condition is needed, we find that our forecast is improved by merging tsunami forward modeling from a rapid W‐phase earthquake source solution with the data assimilation method.