Meteotsunamis, often overlooked in global disaster discussions, pose significant threats to coastal communities and infrastructure, particularly in shallow coastlines, narrow bays, and harbors. Unlike seismic tsunamis, where source parameters can typically be determined quickly following an earthquake, forecasting meteotsunamis remains challenging due to difficulties in obtaining real-time high-resolution atmospheric pressure data that contain meteotsunami source parameters. Intense short-period atmospheric pressure disturbances are typical triggers of meteotsunamis, but these small-scale pressure anomalies are often missed in observations and numerical weather forecasts. This study explores the feasibility of using weather radar reflectivity as a proxy for atmospheric pressure anomalies to enhance meteotsunami forecasting capabilities. The study employs the Method of Splitting Tsunamis (MOST) model, commonly used for seismic tsunami forecasting, across three very different regions: the East Coast of the United States, the southeastern Baltic Sea, and the Adriatic Sea (the Mediterranean). The results demonstrate that radar-based modeling successfully captures meteotsunami generation and propagation dynamics, particularly for summer events driven by mesoscale convective systems. The findings suggest that real-time meteotsunami forecasting is achievable using weather radar inputs, providing a promising approach for coastal hazard mitigation and early warning systems. However, winter meteotsunamis which are often related to extratropical cyclones and wind-driven forces, and which are superimposed on ongoing storm surges require additional modeling refinements.

• The 2022 Tonga eruption confirmed that explosions can produce air-driven tsunamis • Lamb waves from large explosions are one way to generate and drive global tsunamis • Airbursts from comets and asteroids can produce tsunamis by atmospheric coupling • Several mechanisms are proposed for air-driven tsunami generation and amplification • Impact plume collapse, vortices, and steam blowoff are hypothetical tsunami sources

Tsunamis generated by volcanic eruptions have risen to prominence since the December 2018 tsunami generated by the flank collapse of Anak Krakatau during a moderate eruption and then the global tsunami generated by the explosive eruption of the Hunga volcano in the Tongan Archipelago in January 2022. Both events cause fatalities and highlight the lack in tsunami warning systems to detect and warn for tsunamis induced by volcanic mechanisms. Following the Hunga Tonga—Hunga Ha’apai eruption and tsunami, an ad hoc working group on Tsunamis Generated by Volcanoes was formed by the Intergovernmental Oceanographic Commission of UNESCO. Volcanic tsunamis differ from seismic tsunamis in that there are a wide range of source mechanisms that can generate the tsunamis waves and this makes understanding, modelling and monitoring volcanic tsunamis much more difficult than seismic tsunamis. This paper provides a review of both the mechanisms behind volcanic tsunamis and the variety of modelling techniques that can be used to simulate their effects for tsunami hazard assessment and forecasting. It gives an example of a volcanic tsunami risk assessment undertaken for Stromboli, outlines the requirement of volcanic monitoring to warn for tsunami hazard and provides examples of volcanic tsunami warning systems in Italy, the Hawaiian Island (USA), Tonga and Indonesia. The paper finishes by highlighting the need for implementing monitoring and warning systems for volcanic tsunamis for locations with submarine volcanoes or near-shore volcanoes which could potentially generate tsunamis.

An extraordinarily powerful, explosive eruption occurred from Hunga volcano in the Tonga island arc on 15 January 2022 and generated an eruption column 58 km high. The explosive eruption also generated atmospheric gravity waves, extreme runup tsunamis and quite unusual and destructive meteotsunamis. Together these place this VEI 6 eruption as, globally, one of the largest of the past 300 years Based on the oceanic context of Hunga volcano, it has previously been assumed that the eruption was phreatomagmatic through a fuel-coolant Surtseyan-type interaction, but this is not supported by satellite imagery. Similarly, it has been suggested that a caldera-collapse was the eruption trigger, but this is not supported by bathymetric data or the seismicity recorded during the eruption. Here we develop a new model based on the observed energetics and time sequence of the eruption integrated with understanding of the internal structure of active volcanoes and their characteristic high flux discharges of volcanic gas. It has been shown elsewhere that magma-derived reactive gases (H2O, CO2, SO2, HCl, etc) aggressively alter the volcanic rocks in the core of a volcano leading to selfsealing of gas flow to the surface and consequent changes to deviatoric stress in the structure. Common minerals developed by these reactions include anhydrite (CaSO4), sulphides and silica (quartz). all of which have been recorded in volcanic ejecta including at Hunga. We here develop a first order numerical model that quantifies how the free discharge of such gas to the surface may progressively become choked by these sealing reactions leading to increased internal gas pressure. Hydraulic fracture of the seal occurs when the transmitted pressure of the compressed magmatic gas beneath the seal increases to a value greater than the lithostatic pressure plus the tensile strength of the sealed rock. This initiates the explosive release of compressed gas whose high-power discharge progressively develops and enlarges a crater. At the same time the explosion feeds upon itself by generating larger pressure gradients in the pressurized gas within the fractured porous rock mass of the core of the volcano. Excavation of the crater may intersect high level intrusions and produce the pumice rafts that were observed after the eruption. The eruption itself diminished in intensity as the gas pressure in the reservoir declined. At Hunga the eruption excavated an 850 m deep, 2-3 km diameter steep-walled crater. This volume may be assumed to approximate the volume of fractured porous rock (the control volume of the eruption) whose trapped gas was mined by the eruption until surrounding gas pressure was depleted. Our numerical model shows that the calculated potential energy of the trapped compressed gas matches the independent observations of the scale of the eruption. Sensor data have since shown that gas bubble flares continued for at least 6 months after the eruption indicating continued depletion of the gas reservoir of rocks surrounding the new crater. The systems-based, gas-driven model for the Hunga climactic eruption developed here also applies to Plinean-type eruptions on subaerial arc volcanoes such as at Pinatubo (Philippines) 1991.

The large wave caused by the dinosaur-killing asteroid impact eroded the seafloor and disturbed sediments in ocean basins far from the collision site.

NOAA Pacific Marine Environmental Laboratory’s (PMEL’s) approach to tsunami research is unique among such laboratories in that tsunami observations and modeling are under one roof, offering the advantages of enhancing the speed and lowering the cost of developments. Here, we chronicle the history of the transfer of deep-ocean observational and flooding modeling technologies within and outside of NOAA and provide a case study for future transfers. PMEL and partners’ efforts in transferring tsunami technology have been very successful, resulting in improved protection of global communities with high tsunami risk while enhancing the new blue economy. The transfer of observational technology within NOAA required years of effort, while the transfer outside of NOAA only required a patent and license agreement. During the transfer process, three additional generations of observational technologies were created. The transfer of tsunami flooding modeling technology required a validation process for transfer into NOAA operations and an international training program to allow access to the technology by other countries. During this model development, a web-based product was created to simplify the use of and access to these models for both real-time and hazard assessment applications. We present lessons learned from these transfers, including the need for support as long as the technology is in use. The tsunami transfer process created a wealth of economic expansion while protecting coastal citizens from future tsunamis.

This article chronicles the 50-year history of tsunami research and development at the NOAA Pacific Marine Environmental Laboratory (PMEL), beginning with the merger in 1973 of the Joint Tsunami Research Effort and PMEL. It traces the development of instrumentation and modeling that brought a better understanding of tsunamis and improved warning systems. The advantage of having observational engineering and flooding modeling under one roof are highlighted. Deep-ocean Assessment and Reporting of Tsunami (DART) research and development led to technology transfer to NOAA’s National Data Buoy Center (NDBC) that now operates and maintains 39 buoys and serves as real-time data distributor for other nations. This technology was also patented and licensed by PMEL to meet the needs of the international community. DART licensee Science Applications International Corporation (SAIC) has manufactured over 60 buoys for eight different countries. DART data are essential for accurate tsunami warnings, so the global society benefits by receiving lifesaving information before the arrival of a tsunami. PMEL’s tsunami flooding modeling research led to technology transfer to NOAA’s tsunami warning centers, the National Tsunami Hazard Mitigation Program, and international tsunami preparedness communities. Short-term flooding modeling research was initiated at PMEL to improve NOAA tsunami warning operations to better serve US coastal communities. The same validated modeling technology was then applied to produce hazard maps for coastal communities in the United States and internationally through the United Nations’ Intergovernmental Oceanographic Commission (IOC). Tsunami hazard maps are an essential first step in preparing a community for the next tsunami. Using these maps and other preparedness criteria, a community can become “Tsunami Ready” for the next event. Tsunami Ready has been adopted by the IOC as the global standard for preparedness of at-risk communities with total populations exceeding 890 million people.

Abstract The Chicxulub crater is the site of an asteroid impact linked with the Cretaceous‐Paleogene (K‐Pg) mass extinction at ∼66 Ma. This asteroid struck in shallow water and caused a large tsunami. Here we present the first global simulation of the Chicxulub impact tsunami from initial contact of the projectile to global propagation. We use a hydrocode to model the displacement of water, sediment, and crust over the first 10 min, and a shallow‐water ocean model from that point onwards. The impact tsunami was up to 30,000 times more energetic than the 26 December 2004 Indian Ocean tsunami, one of the largest tsunamis in the modern record. Flow velocities exceeded 20 cm/s along shorelines worldwide, as well as in open‐ocean regions in the North Atlantic, equatorial South Atlantic, southern Pacific and the Central American Seaway, and therefore likely scoured the seafloor and disturbed sediments over 10,000 km from the impact origin. The distribution of erosion and hiatuses in the uppermost Cretaceous marine sediments are consistent with model results.

Earthquake-triggered giant tsunamis can cause catastrophic disasters to coastal populations, ecosystems and infrastructure on scales over thousands of kilometres. In particular, the scale and tragedy of the 2004 Indian Ocean (about 230,000 fatalities) and 2011 Japan (22,000 fatalities) tsunamis prompted global action to mitigate the impacts of future disasters. In this Review, we summarize progress in understanding tsunami generation, propagation and monitoring, with a particular focus on developments in rapid early warning and long-term hazard assessment. Dense arrays of ocean-bottom pressure gauges in offshore regions provide real-time data of incoming tsunami wave heights, which, combined with advances in numerical and analogue modelling, have enabled the development of rapid tsunami forecasts for near-shore regions (within 3 minutes of an earthquake in Japan). Such early warning is essential to give local communities time to evacuate and save lives. However, long-term assessments and mitigation of tsunami risk from probabilistic tsunami hazard analysis are also needed so that comprehensive disaster prevention planning and structural tsunami countermeasures can be implemented by governments, authorities and local populations. Future work should focus on improving tsunami inundation, damage risk and evacuation modelling, and on reducing the uncertainties of probabilistic tsunami hazard analysis associated with the unpredictable nature of megathrust earthquake occurrence and rupture characteristics.

Meteotsunamis are created by transitory weather disturbances moving over water, have a long history of impacting the United States (U.S.) and have resulted in loss of life and property. Many of these events have been historically mischaracterized as seiches, anomalous weather-related waves, or ignored altogether. In this paper, we review meteotsunami generation mechanisms common in the U.S. and highlight several classic historical cases of U.S. meteotsunami formation and impact. We then describe recent advances in sensing and understanding that led to the establishment of initial, rudimentary alerting capabilities for the U.S. Great Lakes and U.S. East Coast. Finally, we describe the major challenges and gaps that must be overcome to move the U.S. toward a comprehensive meteotsunami forecast and warning capability. We also discuss how we envision the various relevant offices of the national oceanic and atmospheric administration (NOAA) working together to achieve this vision. These offices include the NOAA research laboratories, national weather service (NWS) Weather Forecast Offices and National Centers, National Ocean Service Center for Operational Oceanographic Products and Services and NWS Tsunami Warning Centers.

A modeling study has been conducted to simulate the June 13, 2013 U.S. East Coast meteotsunami event as a test of the model forecast concept. A numerical simulation based on the MOST (Method of Splitting Tsunami) model was employed for the meteotsunami propagation forecast, while the weather radar reflection imagery was used to simulate real-time input data for the atmospheric pressure-induced tsunami generation. The model tsunami was generated by a moving pressure field during 2.87 h of forcing, and the resultant tsunami was then simulated for additional 5.68 h of propagation without any forcing for a total of 8.55 h of meteotsunami evolution from generation to coastal impact. Simulated time series were compared with the measurements from sea-level coastal gages and the Deep-ocean Assessment and Reporting for Tsunami (DART) data. The model is able to reproduce in general the recorded sea-level changes in the deep ocean and at the coast in terms of arrival times and amplitudes. The model was able to predict coastal tsunami impacts that occurred from one to two hours after the model data assimilation phase ended. Therefore, this approach shows promise for developing meteotsunami model forecast capability based on measurements and data assimilation in real time, at least for meteotsunamis generated by fast-moving weather systems visible on radar reflection imagery. All the data used in this study are already available in real time, the MOST model is already implemented as a seismically generated tsunami forecast model at the Tsunami Warning Centers (TWCs), which makes transition of potential meteotsunami forecast capability to warning operations straightforward.

Within 4 months of 2018, two fatal tsunamis struck islands of Indonesia with ferocity that astonished local population, tsunami warning systems and scientists. For both of these events, the September 28 Palu Bay tsunami in Sulawesi and the 22 December Anak Krakatau tsunami in Sunda Strait, the initial tsunami source data was either non-suggestive or simply non-existent to imply such a devastating wave impact. International teams of scientists, members of the International Tsunami Survey Team, descended to Indonesia to help local scientists collecting all possible data from these two events, investigating the origins of these tsunamis to explain the unexpected tsunami strength. The analysis of the observation data presented in this collection of papers mostly explains the unexpectedly devastating impact from these two unusual tsunami events. The lessons learned from the response to these two events coupled with the new scientific understanding of tsunami genesis will provide improved guidance for more effective tsunami warning operations for Indonesia and the coastlines around the World.

Tsunami hazard is a unique challenge. The opportunities of the new blue economy with wide partnerships between private industry and governments may be the needed catalyst to finally resolve the century-old tsunami warning problem of protecting lives and property against this deceptive and deadly hazard. Tsunamis are one of the most destructive and deadly natural hazards with potential economic liabilities in billions of dollars and countless lives at risk. Nevertheless, long-term tsunami risk from these rare events may not be fully recognized due to their rare occurrence. The knowledge-based new blue economy will not only better quantify the tsunami risks but will also provide new economic incentives, tools, and partnerships for developing tailored tsunami warning products.

On September 29, 2009 at 17:48 UTC, an M-w = 8.1 earthquake in the Tonga Trench generated a tsunami that caused heavy damage across Samoa, American Samoa, and Tonga. One of the worst hits was the volcanic island of Tutuila in American Samoa. Tutuila has a typical tropical island bathymetry setting influenced by coral reefs, and so the event provided an opportunity to evaluate the relationship between tsunami dynamics and the bathymetry in that typical island environment. Previous work has come to differing conclusions regarding how coral reefs affect tsunami dynamics through their influence on bathymetry and dissipation. This study presents numerical simulations of this event with a focus on two main issues: first, how roughness variations affect tsunami run-up and whether different values of Manning's roughness parameter, n, improve the simulated run-up compared to observations; and second, how depth variations in the shelf bathymetry with coral reefs control run-up and inundation on the island coastlines they shield. We find that no single value of n provides a uniformly good match to all observations; and we find substantial bay-to-bay variations in the impact of varying n. The results suggest that there are aspects of tsunami wave dissipation which are not captured by a simplified drag formulation used in shallow-water waves model. The study also suggests that the primary impact of removing the near-shore bathymetry in coral reef environment is to reduce run-up, from which we conclude that, at least in this setting, the impact of the near-shore bathymetry is to increase run-up and inundation.

A performance measure for a DART tsunami buoy network has been developed. The measure is based on a statistical analysis of simulated forecasts of wave heights outside an impact site and how much the forecasts are degraded in accuracy when one or more buoys are inoperative. The analysis uses simulated tsunami height time series collected at each buoy from selected source segments in the Short-term Inundation Forecast for Tsunamis (SIFT) database and involves a set for 1000 forecasts for each buoy/segment pair at sites just offshore of selected impact communities. Random error-producing scatter in the time series is induced by uncertainties in the source location, addition of real oceanic noise, and imperfect tidal removal. Comparison with an error-free standard leads to root-mean-square errors (RMSEs) for DART buoys located near a subduction zone. The RMSEs indicate which buoy provides the best forecast (lowest RMSE) for sections of the zone, under a warning-time constraint for the forecasts of 3 hrs. The analysis also shows how the forecasts are degraded (larger minimum RMSE among the remaining buoys) when one or more buoys become inoperative. The RMSEs also provide a way to assess array augmentation or redesign such as moving buoys to more optimal locations. Examples are shown for buoys off the Aleutian Islands and off the West Coast of South America for impact sites at Hilo HI and along the U.S. West Coast (Crescent City CA and Port San Luis CA). A simple measure (coded green, yellow or red) of the current status of the network's ability to deliver accurate forecasts is proposed to flag the urgency of buoy repair.

The 2004 Sumatra tsunami was an unprecedented global disaster measured throughout the world oceans. The present study focused on a region of the southeastern Pacific Ocean where the westward circumferentially propagating tsunami branch converged with the eastward branch, based on data from fortuitously placed Chilean DART 32401 and tide gauges along the coast of South America. By comparison of the tsunami and background spectra, we suppressed the influence of topography and reconstructed coastal spectral ratios that were in close agreement with a ratio at DART 32401 and spectral ratios in other oceans. Findings indicate that even remote tsunami records carry spectral source signatures (birth-marks). The 2004 tsunami waves were found to occupy the broad frequency band of 0.25-10 cph with the prominent ratio peak at period of 40 min related to the southern fast-slip source domain. This rupture hot-spot of approximate to 350 km was responsible for the global impact of the 2004 tsunami. Data from DART 32401 provided validation of model results: the simulated maximum tsunami wave height of 2.25 cm was a conservative approximation to the measured height of 2.05 cm; the computed tsunami travel time of 25 h 35 min to DART 32401, although 20 min earlier than the actual travel time, provided a favorable result in comparison with 24 h 25 min estimated from classical kinematic theory. The numerical simulations consistently reproduced the wave height changes observed along the coast of South America, including local amplification of tsunami waves at the northern stations of Arica (72 cm) and Callao (67 cm).