Abstract. The eastern Adriatic coast is a known hotspot of strong meteorologically induced high-frequency sea-level oscillations, occurring at periods shorter than 1 hour and reaching wave heights of several metres. When highest, these oscillations are termed meteotsunamis. In this study, we test deep-learning methods for predicting maximum daily amplitudes of high-frequency (T < 1 hour) sea-level oscillations at two Adriatic locations, Bakar and Ploče, using convolutional neural networks driven by past sea-level observations and atmospheric predictors from the ERA5 and CERRA reanalyses. We evaluate two deep-learning architectures designed to test different approaches to representing sea-level and atmospheric forcing. The first architecture, HFNet, is based on the HIDRA family of models, a general low-frequency sea-level forecasting framework that has been extensively evaluated in the Adriatic and shown to provide a credible baseline for sea-level prediction. The second architecture, HFNetJE, extends this approach through joint encoding of atmospheric predictors and a more extensive processing of past sea-level information, with the aim of improving the representation of processes associated with high-frequency sea-level oscillations. Analysis of more than 20 years of data shows that high-frequency sea-level extremes are larger in Bakar (> 60 cm) than in Ploče (< 35 cm), occur ~6 times per year, and are most common during the warm season. Both architectures reproduce the observed variability, with higher skill for typical than for extreme events. HFNetJE performs best overall and under typical amplitude conditions, whereas HFNet more effectively captures extreme events, although these remain systematically underestimated in both architectures. Model performance is higher at Ploče, likely because of its smaller sea-level range and simpler response to atmospheric forcing. Models forced with ERA5 consistently outperform those using the higher-resolution CERRA in predicting extremes, suggesting limited added value from increased spatial resolution. Ablation experiments indicate that several predictors are redundant for average forecasting performance, whereas extreme-event prediction generally benefits from the full predictor set. Overall, the results demonstrate the potential of deep learning for prediction of high-frequency sea-level oscillations in the Adriatic, but also highlight persistent limitations in forecasting rare high-amplitude events.

Split International Airport (LDSP), situated on the eastern Adriatic coast, presents a complex meteorological environment for aviation operations. The airport's location at the base of the Dinaric Alps creates a unique intersection between synoptic-scale flows and sub-mesoscale coastal circulations.Following a collaboration between the University of Split, Split Airport, and Croatia Control (air navigation service provider) continuous SODAR (SOnic Detection And Ranging) measurements have been operational since June 2022 at the airport location. The primary objective of this university project was the collection of research data, but since it was installed at the airport, it’s real-time measurements can also provide very useful information for aviation forecasters.The data is collected using a Scintec MFAS (Multi-Frequency Flat Array SODAR). While the instrument is capable of a vertical range up to 1000 m, it is optimized for high-resolution monitoring within the lowest 500 m of the troposphere—the most critical zone for aircraft approach and departure. Data is processed at 20-minute intervals, providing a detailed vertical profile of wind direction and speed.The four-year dataset captures the seasonal and diurnal variability of the four dominant local wind regimes:Bora (Bura): A gusty north-easterly wind, often associated with severe mechanical turbulence due to the nearby mountains.Sirocco (Jugo): A moist, south-easterly flow, sometimes with wind speeds above 25m/s in the 500m surface layer.Sea-Breeze (Maestral): A predictable but significant south-westerly coastal circulation that dictates runway changes from standard instrumental approach to a more complex visual approach from north-east, due to mountain proximity on that side of the airport.North-westerly gap flow : Katabatic flow from the mountain gap north-west of the airport, but also a tramontana, typical post-frontal flow (synoptic + orography).Moreover, in aviation meteorology information from SODAR instrument can be very useful for detecting vertical wind shear.This poster will present some typical examples of wind profiles at Split airport, wind roses at different altitudes (e.g., 50m, 150m, 300m), and examples of significant wind shear episodes.These findings emphasize the necessity of ground-based remote sensing for enhancing safety and operational efficiency at topographically challenged coastal airports.

Qualitative analysis of synoptic conditions associated with extreme high-frequency sea level oscillations recorded at selected Mediterranean tide gauge stations is presented. Two types of extreme events are considered: (1) events in which high-frequency component is dominant component of the residual sea level height; and (2) events in which the contributions of both high-frequency and low-frequency component to residual signal are nearly equal. We show that, on average, events of type (1) are accompanied by westerly winds at 500 hPa height, north-to-south temperature gradient at 850 hPa, and weak gradients of mean sea level pressure field. Events of type (2) are, on average, characterized by south-westerly winds at 500 hPa height, inflow of warmer, southern air from Africa towards the affected regions, detectable at 850 hPa height, and more enhanced gradients of mean sea level pressure. Further sub-classification of both types of events, based on the wind direction at 500 hPa height is proposed. Three subtypes of events are considered for each of the two groups, events characterized by (a) north-westerly; , (b) south-westerly, and (c) westerly winds. For events of type (1) we find that subtype (a) is characterized by the advection of colder air of northern latitudes over affected areas at 850 hPa height and strong gradients in mean sea level pressure field, caused by high-pressure fields found to the west and low-pressure fields to the east from the affected areas. In contrast, for subtype (b) we observe the inflow of warmer, southern air towards the affected areas at 850 hPa height and mean sea level pressure lows at and around all affected locations. Subtype (c) is characterized by mostly homogenous mean sea level pressure fields, with typical north-to-south temperature gradients at 850 hPa. For events of type (2), we observe that all three subtypes of events qualitatively resemble the subtypes discussed in context of events of type (1). However, air pressure lows observed in mean sea level pressure field in case of type (2) events are noticeably deeper. These new findings are expected to contribute to overall understanding of synoptic conditions driving different types of sea level extremes, potentially leading to further development in forecasting of these events.

Extreme sea-level events occur across a range of temporal and spatial scales, including high-frequency oscillations (periods T < 2 h). However, most research uses hourly or daily data and neglects higher-frequency processes such as seiches and meteotsunamis. This study investigates the characteristics of high-frequency (HF) sea-level extremes along the North Sea coast. Long-term (1993–2025) measurements were analysed from 29 tide gauges. After quality control, the astronomical tide was removed to isolate residuals, which were decomposed into low-frequency (T > 2 h) and high-frequency (T < 2 h) components. Next, extremes were extracted from the HF signal alone (HF extremes) and from the residual (residual extremes). Clustering techniques (K-medoids with Dynamic-Time-Warping and Euclidean distances) were applied to HF extremes to classify event types and identify regional patterns. HF extremes were grouped into six event types, and selected stations yielded five spatial clusters. Each event type was characterised by period, intensity, and its relationship to residual extremes. HF extremes were generally low compared with tidal ranges, and compound events (i.e., events in which an HF extreme coincided with a residual extreme) were infrequent (12.2

A series of four storms struck the coast of British Columbia (BC) during November 2024. The storms (cyclones) produced significant storm surges, intense seiches, marked infragravity waves, and shifting current patterns. Different types of sea level oscillations prevailed, depending on the atmospheric forcing, local topographic properties, and resonant shelf/coastal zone features. The strongest of the four events was the third storm (here, “bomb cyclone”) during 18–21 November. The November 2024 bomb cyclone had extremely low air pressure of 942 hPa in the cyclone center and maximum wind gusts of more than 160 km/h. The storm made landfall on the west coast of the United States and Canada, but fortunately then made a loop and turned to the southwest, sparing the BC coast from the most hazardous effects. For the present study, we examined 35 tide gauge and 9 air pressure records from stations located along the BC coast, as well as data from an open-ocean weather station situated near the entrance to Juan de Fuca Strait. The lowest atmospheric pressure of 978.5 hPa was measured at Daajing Giids (Haida Gwaii) during Cyclone 1, but the sharpest decrease in air pressure (about 20 hPa per 4 h) was observed at Tofino at the time of the “bomb cyclone”. The entire outer coast of Vancouver Island was affected by this cyclone, where minimum air pressures reached 985–987 hPa and was accompanied by marked HF air pressure oscillations. The highest storm surges, up to 60 cm, were measured in this particular region. Also, during the “bomb event”, prominent seiches of 25–30 cm were observed at the stations located in this region (Tofino, Ucluelet, Port Alberni and Bamfield), while maximum infragravity wave heights of 68 cm were recorded at Port Renfrew. The seiche duration was prolonged and their periods corresponded to the resonant periods of the respective basins estimated from long-term background sea level series at these stations. Periods were mostly < 30 min, except at Port Alberni, where the recorded seiches had periods of 100–110 min, corresponding to the fundamental (Helmholtz) period of Alberni Inlet. Prominent infragravity waves with periods < 10 min recorded at Port Renfrew were found to be highly correlated with storm waves measured at the meteorological buoy and wave station.

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.

Intense high-frequency sea-level oscillations (HFOs) in the Mediterranean Sea, sometimes leading to destructive meteotsunamis, are generated by specific meteorological conditions that are spatially limited (from several tens to a few hundred kilometers). Although the physical mechanisms driving extreme HFOs are well understood, existing forecasting systems based on hydrodynamic models remain unreliable and computationally demanding.To address these limitations, we developed deep-learning models (CNNs and ViTs) to predict HFOs using data from the Adriatic tide-gauge station Bakar, which provides a long record (2003–2025) but is not particularly prone to meteotsunamis. Models trained at Bakar can, however, be transferred to meteotsunami-prone Adriatic locations with shorter data records (Stari Grad, Vela Luka, Mali Lošinj, Sobra, etc.). Models were trained using measured 1-minute sea levels together with two sources of simulated atmospheric data (2D and 3D fields): hourly ERA5 data at 30 km resolution and 3-hourly CERRA data at 5.5 km resolution.We will present model architectures and predictions of HFO amplitudes as a function of (i) forecasting horizon (up to several days, using different input windows of 6 h and 24 h), (ii) atmospheric data source (ERA5 vs. CERRA), and (iii) different combinations of training, validation, and testing periods. The main findings are as follows: (i) daily HFO amplitudes remain reasonably predictable over multi-day horizons, with comparable results from CNN and ViT approaches; (ii) forecast skill is higher for low-amplitude HFOs (up to ~12 cm); (iii) higher-amplitude events (10–40 cm) are generally underestimated; (iv) higher-resolution atmospheric forcing (CERRA) does not improve forecast skill, suggesting that meteotsunami-triggering atmospheric disturbances are not better represented at higher resolution; and (v) the choice of training, validation, and testing intervals has little effect on forecasting of small-amplitude events but affects forecasts of larger-amplitude HFOs.

In the present climate, where sea levels are rising, investigating high-frequency sea level extremes is of great importance due to the necessity of timely protection of coastal communities and their infrastructure. In this study, high-frequency filtered (HF, T < 2 h) sea level records from 224 stations and space-based measurements (Spinning Enhanced Visible Infra-Red Imager (SEVIRI) flown on Meteosat-9, -10), together with the regional climate model version 4.6 (RegCM 4.6), are used to assess the atmospheric state during HF sea level extremes recorded over climatologically different parts of Europe. The results show that during winter and summer months, variables such as sea level pressure, surface wind speed and geopotential height act in the opposite direction at most stations, indicating a substantial difference in atmospheric state between winter and summer extremes. This finding points to different mechanisms driving HF extremes formation in different seasons. A contrast in atmospheric state is also observed between northern and southern regions. Analysis of storm activity during HF extremes using SEVIRI revealed a presence of convective storm activity, an area where brightness temperature (Tb) is below 255 K and presence of a very deep convective storms (Tb < 235 K) for the majority of regions during HF extreme events.

We model the long-duration storm surges generated by October-November 2023 storm chain in the English Channel employing a hybrid method including numerical modelling, field surveys and analysis of oceanic and atmospheric data. The event consisted of three successive storms: a weaker unnamed storm (27-30 October), Storm Ciaran (2-3 November with minimum pressure 948 hPa) and Storm Domingos (4-5 November with minimum pressure 958 hPa). The average surge duration produced by this storm chain was 10.5 days. The average maximum air pressure drop was 42 hPa during Ciaran and 23 hPa during Domingos. These pressure drops, combined with onshore wind stresses, led to average maximum storm surge amplitudes of 92 cm for Ciaran and 74 cm for Domingos. We accurately modelled storm surges using a three-level nested grid system and validated the results with tide gauge data. Sensitivity analysis showed a spatially-dependant impacts from tides and waves on maximum surge amplitudes. To correlate our modelling and data analysis with actual conditions on the ground, field surveys were conducted where we measured a runup heights of 4.1 m in Chesil Beach and 2.1 m in West Bay. These values were successfully reproduced by two independent empirical runup models enabling adaption of suitable models for storm hazard mitigation and resilience. A meteotsunami with an amplitude of 17-23 cm and a period of 11-40 min was identified during Ciaran. The innovative hybrid framework developed in this study is recommended for building robust systems for storm warnings and coastal resilience.

Abstract. Availability of high-quality sub-hourly sea level data is essential for understanding of a wide range of oceanic processes, including tidal oscillations, seiches, storm surges, tsunamis (including meteotsunamis), and their impact on sea level extremes and coastal flooding. Freely accessible sea level databases often contain time series measured with hourly or even longer sampling step, or they contain high-frequency data that have not undergone quality control procedures. To address this gap, the SHELDA (Sub-Hourly European Quality Controlled Sea Level DAtaset) has been created. This dataset comprises 257 individual tide gauge records in NetCDF format (https://doi.org/10.14284/764, Balić and Šepić, 2025), each representing quality-controlled sea level time series sampled at intervals between 1 and 15 minutes, along with residual time series derived by removing tidal components. This paper outlines the rigorous quality control procedures implemented and describes the spatial and temporal coverage of the dataset, along with technical specifications. SHELDA enables precise identification and analysis of sea level variability at timescales from minutes to multi-yearly along the European coasts, including Greenland, Canary Islands, Israel, Lebanon and Türkiye.

Sea-level extremes represent a great danger to coastal infrastructure and a daily threat to people living near the coast. These extremes are predicted to become more frequent in the coming years and decades, mostly due to mean sea-level rise. Knowledge of the underlying principles that drive these events is, thus, of greater importance than ever. Our analysis focuses on events of high-frequency sea-level extremes (extremes at periods shorter than 2 hours). Extreme events were extracted from sea level data series measured at six Adriatic Sea tide gauge records. Series lengths were from 16 to 17.5 years. The sea-level data series were split into a training set and a testing set. Splitting was done so that approximately 80% of the series were used for the training and remaining 20% for the testing. K-means classification was then used to associate extremes events of the training period with atmospheric synoptic conditions, represented with the synoptic variables downloaded from the ERA5 reanalysis. The atmospheric variables considered were the ones found by earlier research to be the most important when it comes to generation of intense high-frequency sea-level oscillations. These variables are: (i) temperature at 850 hPa, (ii) mean sea-level pressure and wind at 10 m and (iii) geopotential at 500 hPa. K-means classification was used to find prevailing clusters related to extremes at each of the six tide gauges. After that, the same synoptic variables were downloaded for each day of the testing period. To each tide gauges, and to each day of the testing period, a cluster, previously defined for the training period, was assigned. The idea was to check whether days of known extremes will be correctly clustered. The goodness of the approximations was determined by estimating the distance of the synoptic maps from the clusters. The results show that the testing period days with extremes have a smaller distance from the clusters than random days indicating that there is a potential for prediction of these events.

Intense high-frequency sea-level oscillations (HFOs) in the Mediterranean Sea, sometimes leading to destructive meteotsunamis, occur due to specific and spatially limited meteorological conditions. Despite the understanding of their physical dynamics, current forecasting systems based on hydrodynamic models are unreliable and computationally demanding. To address this problem, we built deep-learning models of HFOs for the Adriatic tide-gauge stations with long measurement records (Bakar and Ploče) and transferred these models to meteotsunami-prone locations with limited data (Stari Grad, Vela Luka and Sobra). We trained deep convolutional neural networks using simulated data (hourly mean sea-level pressure, geopotential heights, specific humidity, wind speed, air temperature from ERA5, and the calculated Richardson number) alongside measurements (1-min sea levels). We will present the model's architecture, transfer learning results, and predictions of HFO amplitudes based on: (i) forecasting horizons (ranging up to several days with different time windows; 6 h vs. 24 h), (ii) data inputs (total sea level vs. sea level decomposed into components), and (iii) various refinement strategies through inclusions of additional U-net based refinement heads. The results demonstrate that the developed models can predict the highest expected HFO amplitudes for the next three days with reasonable accuracy. Accuracy improves when using the ‘wet’ Richardson number instead of the ‘dry’ version, extending time windows (e.g., targeting the largest amplitude in the overall next 24 h rather than every 6 h), and reducing the input dataset. Performance also varies depending on the station from which the model was transferred. In all cases, the forecast accuracy is higher for smaller HFO amplitudes, with refinements primarily improving predictions of smaller amplitude HFOs.

Abstract. Flooding in the northern Adriatic Sea occasionally occurs in late fall and winter as a result of storm surges that combine with other sea-level processes at different spatial and temporal scales. This paper presents (empirical) analysis of the 27 most intense floods recorded at the Croatian tide-gauge station Bakar on the northeastern coast of the Adriatic Sea in the period 1929–2022. Floods were defined as events in which the hourly sea level rose by at least 89 cm (99.99th percentile threshold) above the long-term average. The study examines: (i) the evolution of sea level, analysed through five components: local processes, tide, synoptic component (storm surge and basin-wide seiche), long-period sea-level variability and mean sea-level changes, (ii) the meteorological conditions, based on reanalysis series and fields (mean sea-level pressure, 10-m wind, 500-hPa surface geopotential heights), (iii) the impact of flooding on natural and built environments along the Croatian coastline, and (iv) the relevant scientific literature on these flood episodes. The study is complemented by the online catalogue, which contains supplementary information and is continuously updated with the latest flooding episodes (https://projekti.pmfst.unist.hr/floods/storm-surges/).

The Adriatic Sea is prone to meteotsunamis, with an exceptionally strong event (wave height > 2 m) observed 1-2 times per decade, and moderate events (wave height > 1 m) once every 1-2 years. Adriatic Sea meteotsunamis occur at many locations along the mainland and, more often, islands. The goal of this research is to determine potential meteotsunami risk along the Adriatic Sea coast. The risk estimate is based on numerical modeling of maximum wave heights in dependance on speed and direction of air pressure disturbances. The modeling results are then combined with the ERA5 reanalysis over the past 30 years to determine how often suitable, previously determined, synoptic conditions for meteotsunamis present over the area. Based on both the sea modeling and atmospheric reanalysis, a meteotsunami hazard level is associated with each point of the Adriatic Sea coast, and the results are shown on a detailed map.

Extreme sea levels in the northern Adriatic Sea occasionally occur in late autumn and winter as a result of storm surges that combine with other sea-level processes of different spatial and temporal scales. This paper presents an (empirical) analysis of the 27 most intense episodes recorded at the Croatian tide-gauge station Bakar on the northeastern coast of the Adriatic Sea in the period from 1929 to 2022. Extreme sea levels were defined as events in which the hourly sea level exceeded 89 cm (99.99th percentile threshold) above the long-term average. The study examines the following: (i) the evolution of sea level, analysed through five components – high-frequency oscillations, tide, synoptic component (storm surge and basin-wide seiche), planetary-scale variability, and long-term sea-level variability; (ii) the meteorological conditions, based on reanalysis series and fields (mean sea-level pressure, 10 m wind, and 500 hPa surface geopotential heights); (iii) the impact of these episodes on the natural and built environments along the Croatian coastline; and (iv) the relevant scientific literature addressing these episodes. The study is complemented by an online catalogue, which contains supplementary information and is continuously updated with the latest extreme episodes (https://projekti.pmfst.unist.hr/floods/storm-surges/, Međugorac et al., 2024b).

An online catalogue of meteorological tsunamis in the Adriatic Sea was recently published. The catalogue contains information on 36 meteorological tsunamis, all with a wave height of at least 1 m, which occurred between 1931 and 2021. During this period, there were 10 exceptionally strong events with observed tsunami wave heights of over 3 metres. The strongest event was characterised by tsunami waves of up to 6 m. For all 36 events, available sea level and air pressure measurements, atmospheric synoptic conditions (using ERA5 reanalysis) and satellite images were analysed. Based on the background sea level height (from the nearest tide gauge), the meteorological tsunamis were divided into three categories: (1) storm surge meteotsunamis, i.e. tsunamis that occur at the time of a storm surge; (2) ordinary meteotsunamis, i.e. tsunamis that occur when the background sea level is low; (3) transitional tsunamis. All three types were associated with a strong south-westerly to westerly jet stream in the middle and upper troposphere, which mainly led to the advection of warm air from the southern Mediterranean to the Adriatic Sea. Similarly, convective clouds were observed over the Adriatic Sea during most events before or at the time of the meteotsunamis. At the surface, three types of events were distinguished from each other. Storm surge meteotsunamis (10 events in total) were associated with a mid-latitude cyclone, centred over the northern Adriatic or the Bay of Genoa, with the cyclone warm sector or advancing cold front over the area affected by the meteotsunami. The associated surface winds were strong and usually of a south-easterly direction (sirocco). The meteotsunamigenic air pressure disturbances were therefore probably generated in the areas of strong updrafts related to the advancing temperature fronts. Ordinary meteotsunamis (21 events) were associated with fair weather, i.e. with a gradient-free mean sea level pressure field over the Adriatic and very weak surface winds. In this type of event, the meteotsunamigenic atmospheric pressure disturbances were probably due to convective disturbances or the atmospheric gravity waves. Transitional events (5 of them) were associated with either a weak gradient of mean sea level pressure field over the Adriatic, with corresponding southeasterly winds of moderate strength, or with a closed shallow low over the Adriatic. Stronger events were more likely to occur under fair weather conditions but were also observed under stormier weather. The analysis suggests that meteotsunamis in the Adriatic occur under variety of conditions, all of which should be considered when assessing the risk of meteotsunamis.

Research on meteorological tsunamis or meteotsunamis—long ocean waves in the tsunami frequency band generated by propagating atmospheric disturbances which resonantly enhance ocean waves—has grown significantly in recent decades. This expansion is due to progress in (a) ocean and atmospheric measurements, including advanced instrumentation with higher precision and smaller sampling time steps, as well as installation of meteotsunami tracking measurement networks, (b) ocean and atmospheric data products, including those related to the upper atmosphere and ionosphere, and (c) supercomputing capabilities and sophisticated atmosphere-ocean models that successfully simulate both atmospheric planetary processes and mesoscale systems capable of generating meteotsunamis, as well as sea level response to these. Meteotsunamis can induce multi-meter sea level oscillations in harbors and low-lying areas, leading to severe flooding, infrastructure damage, injuries, and sometimes fatalities. Traditionally, meteotsunami research focused on individual event analyses using available sea level and lower-layer atmospheric observations. Recently, efforts have shifted toward global hazard mapping, the development of forecast and early-warning systems, and toward quantifying projected meteotsunamis intensity and frequency, using climate models. The January 2022 eruption of the Hunga Tonga-Hunga Ha'apai volcano, which generated acoustic-gravity waves that circled the globe, has spurred research of planetary meteotsunami waves and their potential to pose coastal hazards worldwide. Additionally, meteotsunamis radiate acoustic-gravity waves vertically, creating ionospheric oscillations detectable through electron content variations. This review will cover the mentioned developments and conclude with a discussion of research gaps and potential directions for further studies.

On 26-27 December 2004, a major tsunami, generated by a megathrust earthquake off Sumatra Island, reached the Atlantic Coast of the United States. The arrival of the tsunami coincided with a storm-generated meteotsunami, resulting in a double jeopardy oceanic event. Similar conditions occurred on 15 January 2022, following the eruption of the Tonga-Hunga underwater volcano in the tropical Pacific. The eruption generated atmospheric pressure waves that propagated around the globe several times at roughly the speed of sound. These waves forced pronounced tsunami waves that subsequently impacted the Atlantic Coast of the United States. Almost simultaneously, a deep extratropical cyclone crossed the region on 16-17 January. The cyclone, which had formed over the northern Gulf of Mexico, propagated northeastward as a "bomb cyclone" having a rapid pressure change of 36 hPa (24 h)-1. Strong high-frequency (period < 3 h) atmospheric disturbances accompanied the cyclone. Both the large-scale low and the markedly enhanced atmospheric disturbance reached full strength in the proximity of Atlantic City during the early morning of 17 January. This combination of three hazardous events resulted in a triple jeopardy threat to water levels along the eastern seaboard of the United States, consisting of a cyclone-generated storm surge, a meteotsunami caused by high-frequency atmospheric disturbances, and a tsunami forced by the Tonga air pressure waves. Severe flooding occurred in the Atlantic City region, where sea levels rose by as much as 150 cm above background. Here, we examine the individual properties of the three atmospherically generated processes that gave rise to the cumulatively high sea level response. SIGNIFICANCE STATEMENT: This study examines the coincident arrival of the January 2022 Tonga tsunami, a local meteotsunami, and a storm surge on the Atlantic Coast of the United States. The 2022 Tonga-Hunga volcanic eruption produced tsunami waves that were recorded along the entire East Coast of the United States, over 13 000 km from the source region. A unique aspect of these oceanic waves was their generation by atmospheric waves that several times circumvented the globe. The atmospherically induced Tonga tsunami, combined with the storm surge and local meteotsunami, resulted in a "triple jeopardy" hazard to the U.S. Atlantic Coast. Such events clearly need to be taken into account in any marine hazard warning and mitigation planning.

Extreme sea levels can result in catastrophic flooding of coastal regions, endangering the lives of residents and destroying coastal infrastructure. Due to climate change they are becoming more frequent and therefore more dangerous. Extreme sea levels occur on different temporal and spatial scales, including sub-hourly scales, which we have only recently been able to assess due to the recent enhancement of the temporal resolution of the tide gauge measurements. To quantify the contribution of sub-hourly sea level oscillations to positive sea level extremes, raw sea level data from 288 tide gauges along the European coasts, with a sampling resolution of less than 20 minutes, were obtained from: (1) the IOC-SLSMF website (263 stations); (2) National agencies (Portugal, Finland, Croatia – 24 stations). Large portions of the raw dataset had numerous data quality issues (i.e., spikes, shifts, drifts), thus quality control procedure was required. Out of range values and spikes were automatically removed, remaining data were visually examined, and spurious data were removed manually. After quality control, all data series were de-tided, and residuals were split into a low-frequency (T > 2 h) and a high-frequency (T < 2 h) component. The five highest positive sea level extremes per year were extracted from the residual series and the high-frequency series. These were defined as residual extremes and high-frequency extremes, respectively. The contribution of the high-frequency sea level oscillations to the total sea level extremes along the European coasts was estimated. The contribution was shown to be significantly geographically and station-dependent, and it is important to take it into account when estimating flooding levels.