Tsunamis cause significant damage and loss of life, particularly for the nearest communities, where the tsunami may arrive in minutes. These local communities often do not receive an informed or timely alert under traditional warning pathways. In response, numerous tsunami early warning (TEW) algorithms have been developed with the goal of providing informed tsunami source characterization for use in rapid, localized warning. An overlooked aspect of TEW is the means that this crucial information is disseminated. Current operations focus heavily on the time an alert is issued from a warning center, however, that alert passes through multiple groups and agencies before it is conveyed to affected communities. This distribution path can create further delays and contributes to inconsistencies in the message timeliness and content. In this study, we provide the framework and advocate for the use of a rapid dissemination tool, that we call WaveAlert, that would leverage preexisting advances in earthquake early warning systems to provide timely, clear, and consistent alerts to the public by use of the MyShake EEW phone app. This proposed tsunami dissemination tool would be able to provide consistent, public facing tsunami alerts over the duration of the hazard with the added benefit of low message latencies and high spatial resolution in who can be targeted for messages. We illustrate the need for rapid alerting strategies through a retrospective look at the alerting process during the 2022 Tonga tsunami and through a modeled potential near-field Cascadia timeline example affecting the west coast of the US.

Timely alerts sent through earthquake early warning (EEW) programs allow those alerted to take protective actions to mitigate their risk from potentially damaging shaking. Over the past few years, ShakeAlert, the EEW program focused on the west coast of the con-tiguous United States, has grown, alerting communities within California, Oregon, and Washington about earthquakes where damaging shaking is expected. ShakeAlert uses a set of algorithms including the point-source algorithm, earthquake point-source integrated code (EPIC), to determine the location, magnitude, and origin time of potential earthquakes. Although EPIC produces low-latency and low error solutions for many events originating within the seismic network on land, numerous recent small earthquakes rupturing offshore of northern California have EPIC location solutions with high error ( > 50 km compared to USGS locations). Because most events are occurring offshore, there is a lim-ited number of stations that can trigger and contribute information in a timely manner for use in EEW. To better constrain location solutions in this region, we propose to include information about contemporary past seismicity into EPIC’s grid-search algorithm through a Bayesian framework. This prior information layer downweights high error locations where EPIC’s proposed event location coincides with an area of low prior seismicity in pref-erence for locations with a similar level of data fit that also have higher past seismicity. This addition to EPIC lowers the mean location error offshore northern California from 58 to 14 km.