Estimation of Sea State Parameters from Measured Ship Motions with a Neural Network Trained on Experimentally Validated Model Simulations - Jason M. Dahl; Annette R. Grilli; Stephanie C. Steele; Stephan T. Grilli (Journal of Marine Science and Engineering, 2026)
The use of ships and boats as sea-state (SS) measurement platforms has the potential to expand ocean observations while providing actionable information for real-time operational decision-making at sea. Within the framework of the Wave Buoy Analogy (WBA), this work develops an inverse approach in which efficient simulations of wave-induced motions of an advancing vessel are used to train a neural network (NN) to predict SS parameters across a broad range of wave climates. We show that a reduced set of novel motion discriminant variables (MDVs)—computed from short time series of heave, roll, and pitch motions measured by an onboard inertial measurement unit (IMU), together with the vessel’s forward speed—provides sufficient and robust information for accurate, near-real-time SS estimation. The methodology targets small, barge-like tugboats whose operations are SS-limited and whose motions can become large and strongly nonlinear near their upper operating limits. To accurately model such responses and generate training data, an efficient nonlinear time-domain seakeeping model is developed that includes nonlinear hydrostatic and viscous damping terms and explicitly accounts for forward-speed effects. The model is experimentally validated using a scaled physical model in laboratory wave-tank tests, demonstrating the necessity of these nonlinear contributions for this class of vessels. The validated model is then used to generate large, high-fidelity datasets for NN training. When applied to independent numerically simulated motion time series, the trained NN predicts SS parameters with errors typically below 5%, with slightly larger errors for SS directionality under relatively high measurement noise. Application to experimentally measured vessel motions yields similarly small errors, confirming the robustness and practical applicability of the proposed framework. In operational settings, the trained NN can be deployed onboard a tugboat and driven by IMU measurements to provide real-time SS estimates. While results are presented for a specific vessel, the methodology is general and readily transferable to other ship geometries given appropriate hydrodynamic coefficients.

Tsunami hazard assessment along the US East Coast from extreme sources in the North Atlantic Ocean: a limited probabilistic analysis - Stephan T. Grilli; Lauren Schambach; Maryam Mohammadpour; Annette R. Grilli; James T. Kirby; Edward S. Fratto; Fengyan Shi (Natural Hazards, 2025)
Coastal hazard is assessed along the US East Coast (USEC) through model simulation of 14 probable maximum tsunamis (PMTs) from sources located in the North Atlantic Ocean basin, in terms of: (i) standard inundation maps and other hazard products, and (ii) a limited probabilistic tsunami hazard analysis (PTHA) based on these. Tsunami sources are based on historical records or are hypothetical, including extreme seismic events in the Puerto Rico Trench (PRT)/Caribbean Arc area and in the Açores Convergence Zone (ACZ), near-field submarine mass failures (SMF) on or near the continental shelf break, and large scale volcanic flank collapses of the Cumbre Vieja Volcano (CVV), on La Palma in the Canary Archipelago. Various state-of-the-art models are used to simulate tsunami generation from these sources and propagation to the USEC, in a series of nested grids, by one-way coupling. Based on these results, hazard metrics are computed for each PMT and their envelope as: (i) a synoptic representation along the entire USEC (with results interpolated on the 5 m isobath in coarse 450 m grids), and (ii) high-resolution (10–30 m) inundation maps, for about 30% of the USEC to date, prioritizing the most exposed areas. While existing maps can be accessed in a repository, in this paper, we focus on assessing the synoptic tsunami hazard using 4 hazard metrics (surface elevation, flow velocity, momentum force and arrival time) and their combination into a Tsunami Intensity Index ( TII ) introduced in earlier work. A comparison of metrics for each PMT shows similar alongshore patterns of higher/lower impact, due to wave focusing and defocusing effects induced by the shelf bathymetry; on this basis, 14 focusing areas are identified from Maine to southern Florida, where tsunami hazard for any PMT is always relatively larger. The limited PTHA is then performed based on the PMT metrics, using estimated return period ranges for each source (in the $$T_r\in [500; 50,000]$$ T r ∈ [ 500 ; 50 , 000 ] year interval), from the literature and/or educated guesses. This analysis yields return periods along the USEC for 5 classes of metrics and the TII . The PTHA results are then interpolated to provide the same metrics along the USEC for $$T_r = 500$$ T r = 500 and 2,500 years, which are standard in geohazard assessment. PTHA results show, depending on low-high estimates of PMT return periods that: (i) up to $$T_r = 137-261$$ T r = 137 - 261 years, there is no probable tsunami hazard; (ii) for $$T_r =500$$ T r = 500 years, tsunami hazard would be expected to be low-medium and only larger near some focusing sites, with flooding metrics at par with a severe coastal storm; and (iii) for $$T_r =2,500$$ T r = 2 , 500 years, which is commensurate with the longest return periods considered on the US west Coast for large Cascadia events, the USEC would face high tsunami hazard, with widespread destruction near the focusing areas and severe impact elsewhere. As uncertainties are large on the estimated return period ranges used and, hence, on the results of this limited PTHA, while raising awareness for the potential tsunami hazard facing the USEC, this work stresses the need for conducting a more in-depth and comprehensive PTHA, including using a larger selection of tsunami sources and further supporting and refining the range of return periods selected for each of those. Such work has been initiated and will be the object of future papers.

Uncertainty-Guided Prediction Horizon of Phase-Resolved Ocean Wave Forecasting Under Data Sparsity: Experimental and Numerical Evaluation - Yuksel Rudy Alkarem; Kimberly Huguenard; Richard W. Kimball; Stephan T. Grilli (Journal of Marine Science and Engineering, 2025)
Accurate short-term wave forecasting is critical for the safe and efficient operation of marine structures that rely on real-time, phase-resolved ocean wave information for control and monitoring purposes (e.g., digital twins). These systems often depend on environmental sensors (e.g., waverider buoys, wave-sensing LIDAR). Challenges arise when upstream sensor data are missing, sparse, or phase-shifted due to drift. This study investigates the performance of two machine learning models, time-series dense encoder (TiDE) and long short-term memory (LSTM), for forecasting phase-resolved ocean surface elevations under varying degrees of data degradation. We introduce the τ-trimming algorithm, which adapts the prediction horizon based on uncertainty thresholds derived from historical forecasts. Numerical wave tank (NWT) and wave basin experiments are used to benchmark model performance under short- and long-term data masking, spatially coarse sensor grids, and upstream phase shifts. Results show under a 50% probability of upstream data loss, the τ-trimmed TiDE model achieves a 46% reduction in error at the most upstream target, compared to 22% for LSTM. Furthermore, phase misalignment in upstream data introduces a near-linear increase in forecast error. Under moderate model settings, a ±3 s misalignment increases the mean absolute error by approximately 0.5 m, while the same error is accumulated at ±4 s using the more conservative approach. These findings inform the design of resilient, uncertainty-aware wave forecasting systems suited for realistic offshore sensing environments.

Breaking onset and breaking strength of focused wave packets: Linear prediction model and nonlinear numerical simulations - Florian Hulin; Marc Prevosto; Alan Tassin; Jean-françois Filipot; Nicolas Jacques; Stephan Grilli (Coastal Engineering, 2025)

Assessing the morphodynamic response of a New England beach-barrier system to an artificial reef - Elin Schuh; Annette R. Grilli; Felix Groetsch; Stephan T. Grilli; Deborah Crowley; Isaac Ginis; Peter Stempel (Coastal Engineering, 2023)

Probabilistic landslide tsunami modeling of the 2018 Palu Bay event - Claudia Cecioni; Verdiana Iorio; Giorgio Bellotti; Stephan T. Grilli (Coastal Engineering, 2023)

Unified depth-limited wave breaking detection and dissipation in fully nonlinear potential flow models - Sunil Mohanlal; Jeffrey C. Harris; Marissa L. Yates; Stephan T. Grilli (Coastal Engineering, 2023)

Hybrid Lattice-Boltzmann-Potential Flow Simulations of Turbulent Flow around Submerged Structures - Christopher M. O’Reilly; Stephan T. Grilli; Christian F. Janßen; Jason M. Dahl; Jeffrey C. Harris (Journal of Marine Science and Engineering, 2022)
We report on the development and validation of a 3D hybrid Lattice Boltzmann Model (LBM), with Large Eddy Simulation (LES), to simulate the interactions of incompressible turbulent flows with ocean structures. The LBM is based on a perturbation method, in which the velocity and pressure are expressed as the sum of an inviscid flow and a viscous perturbation. The far- to near-field flow is assumed to be inviscid and represented by potential flow theory, which can be efficiently modeled with a Boundary Element Method (BEM). The near-field perturbation flow around structures is modeled by the Navier–Stokes (NS) equations, based on a Lattice Boltzmann Method (LBM) with a Large Eddy Simulation (LES) of the turbulence. In the paper, we present the hybrid model formulation, in which a modified LBM collision operator is introduced to simulate the viscous perturbation flow, resulting in a novel perturbation LBM (pLBM) approach. The pLBM is then extended for the simulation of turbulence using the LES and a wall model to represent the viscous/turbulent sub-layer near solid boundaries. The hybrid model is first validated by simulating turbulent flows over a flat plate, for moderate to large Reynolds number values, Re ∈[3.7×104;1.2×106]; the plate friction coefficient and near-field turbulence properties computed with the model are found to agree well with both experiments and direct NS simulations. We then simulate the flow past a NACA-0012 foil using a regular LBM-LES and the new hybrid pLBM-LES models with the wall model, for Re = 1.44×106. A good agreement is found for the computed lift and drag forces, and pressure distribution on the foil, with experiments and results of other numerical methods. Results obtained with the pLBM model are either nearly identical or slightly improved, relative to those of the standard LBM, but are obtained in a significantly smaller computational domain and hence at a much reduced computational cost, thus demonstrating the benefits of the new hybrid approach.

Understanding and reducing the disaster risk of landslide-induced tsunamis: a short summary of the panel discussion in the World Tsunami Awareness Day Special Event of the Fifth World Landslide Forum - Shinji Sassa; Stephan T. Grilli; David R. Tappin; Kyoji Sassa; Dwikorita Karnawati; Viacheslav K. Gusiakov; Finn Løvholt (Landslides, 2022)
A World Tsunami Awareness Day Special Event was held in hybrid mode on 5 November 2021, during the Fifth World Landslide Forum, in Kyoto, Japan. In this context, a panel discussion was organized across America, Europe, and Asia, with the goal to better understand and reduce the disaster risk of landslide-induced tsunamis, consistent with the Kyoto Landslide Commitment 2020. This article presents a short summary of this panel discussion.

Tsunami intensity scale based on wave amplitude and current applied to the French Riviera: the case study of local seismicity - Laurie Boschetti; Mansour Ioualalen; Fatemeh Nemati; Stephan Grilli; Jean-Xavier Dessa; Christophe Larroque (Natural Hazards, 2020)

Landslide Tsunami Hazard Along the Upper US East Coast: Effects of Slide Deformation, Bottom Friction, and Frequency Dispersion - Lauren Schambach; Stephan T. Grilli; James T. Kirby; Fengyan Shi (Pure and Applied Geophysics, 2019)

The Global High Frequency Radar Network - Hugh Roarty; Thomas Cook; Lisa Hazard; Doug George; Jack Harlan; Simone Cosoli; Lucy Wyatt; Enrique Alvarez Fanjul; Eric Terrill; Mark Otero; John Largier; Scott Glenn; et al. (Frontiers in Marine Science, 2019)

High-resolution coastal hazard assessment along the French Riviera from co-seismic tsunamis generated in the Ligurian fault system - Fatemeh Nemati; Stephan T. Grilli; Mansour Ioualalen; Laurie Boschetti; Christophe Larroque; Jenny Trevisan (Natural Hazards, 2019)

Far-Field Tsunami Impact in the North Atlantic Basin from Large Scale Flank Collapses of the Cumbre Vieja Volcano, La Palma - Babak Tehranirad; Jeffrey C. Harris; Annette R. Grilli; Stephan T. Grilli; Stéphane Abadie; James T. Kirby; Fengyan Shi (Pure and Applied Geophysics, 2015)

Modeling of SMF tsunami hazard along the upper US East Coast: detailed impact around Ocean City, MD - Stephan T. Grilli; Christopher O’Reilly; Jeffrey C. Harris; Tayebeh Tajalli Bakhsh; Babak Tehranirad; Saeideh Banihashemi; James T. Kirby; Christopher D. P. Baxter; Tamara Eggeling; Gangfeng Ma; Fengyan Shi (Natural Hazards, 2015)

Numerical Simulation of the 2011 Tohoku Tsunami Based on a New Transient FEM Co-seismic Source: Comparison to Far- and Near-Field Observations - Stephan T. Grilli; Jeffrey C. Harris; Tayebeh S. Tajalli Bakhsh; Timothy L. Masterlark; Christodoulos Kyriakopoulos; James T. Kirby; Fengyan Shi (Pure and Applied Geophysics, 2013)

Tsunamis and Challenges for Accurate Modeling - Robert Dalrymple; Stephan Grilli; James Kirby (Oceanography, 2006)