Tyler Hesser

Immediate estimation of nearshore bathymetry is crucial for accurate prediction of nearshore wave conditions and coastal flooding events. However, direct bathymetry data collection is expensive and time-consuming, while accurate airborne lidar-based survey is limited by breaking waves and decreased light penetration affected by water turbidity. Several recent efforts have been made to apply interpolation and inverse modeling approaches to indirect remote sensed observations along with sparse direct survey data points. Example indirect observations include video-based observations such as time-series snapshots and time-averaged (Timex) images across the surf zone taken from tower-based platforms and Unmanned Aircraft Systems (UASs), while stationary LiDAR tower and UAS flights with infrared camera capability or imagery-based structure-from-motion (SfM) algorithms have been used to provide beach topographic data. In this work, we present three bathymetry estimation tools for real-time nearshore characterization using different types of information.

Wave runup is a significant portion of total water levels, particularly during storms (Sallenger, 2000). This makes accurate prediction of wave runup paramount. Wave runup can be estimated using simplified empirical models (e.g. Stockdon et al 2006) or simulated using phase-resolved Bousinesq models (Shi et al 2007, Lynett et al 2002) or non-hydrostatic models (e.g. Zijlema et al 2011). These models typically have a variety of offshore boundary input options ranging from spectral to time series, with the most broadly used being spectral parameterizations (e.g. JONSWAP, TMA, etc.) or direct spectral input, both of which neglect phase information at the offshore boundary. Recent research has highlighted the importance of the offshore boundary condition and wave phase at modeling wave runup (e.g. Fiedler et al 2019, Rutten 2021). This work pays careful attention to the consequences of the bound infragravity (IG) wave and explores the influence that unknown phase information can have on predicted wave transformation and resultant wave runup in the context of field observations.

Soon after the publication of the original manuscript

Landfalling tropical cyclones (TC) generate extreme waves, introducing significant property, personal, and financial risks and damage. Accurate simulations of the sea state during these storms are used to support risk and damage assessments and the design of coastal structures. However, the TCs generate a complex surface gravity wave field as a result of the inherently strong temporal and spatial gradients of the wind forcing. This complexity is a significant challenge to model. To advance our understanding of the performance of these models on the eastern seaboard of the United States, we conduct an assessment of four hindcast products, three based on WAVEWATCH-III and the other using the Wave Modeling project, for six major landfall TCs between 2011–2019. Unique to our assessment was a comprehensive analysis of these hindcast products against an array of fixed wave buoys that generate high quality data. The analysis reveals a general tendency for the wave models to underestimate significant wave height (Hs) around the peak of the TC. However, when viewed on an individual TC basis, distinct Hs error patterns are evident. Case studies of hurricanes Sandy and Florence illustrate complex Hs bias patterns, likely resulting from various mechanisms including insufficient resolution, improper wind input and source term parameterization (e.g., drag coefficient), and omission of wave–current interactions. Despite the added challenges of simulating complex wave fields in shallow coastal waters, the higher resolution Wave Information Study and National Centers for Environmental Prediction (ST4 parameterization only) hindcasts perform relatively well. Results from this study illustrate the challenge of simulating the spatial and temporal variability of TC generated wave fields and demonstrate the value of in-situ validation data such as the north Atlantic buoy array.

Tropical cyclones (TC) are some of the most intense weather systems on Earth and are responsible for generating hazardous waves on the sea surface that dominate the extreme wave climate in several regions, including the Gulf of Mexico and the U.S. East Coast. Modeling these waves is crucial for engineering applications, yet it is notoriously difficult, due to TC’s compact structure and rapid evolution in space and time relative to other weather systems. To better understand the wave structure under TCs, we use satellite altimeter data paired with TC tracks. We parse the data by TC intensity and forward translation velocity, finding evidence of extended fetch. We use the altimeter data to evaluate operational hindcasts, including the US Army Corps of Engineer’s Wave Information Study, National Oceanic and Atmospheric Administration’s National Centers for Environmental Prediction Production Hindcast, and the Institut français de recherche pour l’exploitation de la mer (Ifremer) hindcast. The Ifremer hindcast (1990–2016) is examined in detail. Near the eye in the TC-centered reference frame, we find a pattern of model underestimation in the left sector and overestimation in the right sector except near the eye where wave height remains underestimated. This pattern holds, albeit modulated, across various intensities, forward translation velocities, and radii of maximum winds; the exceptions being the fastest translating storms where the error pattern shows a trend towards overestimation in all sectors. The error patterns for intense and compact TCs exhibited more severe underestimation, which dominated the region near the TC eye.

Accurate estimation of storm surge along the coasts subject to extreme storm conditions requires proper wind and pressure forcing and quantification of the wind waves resulting from local and far-field energy sources. This paper summarizes the steps involved in accurately representing the offshore wave climate for the North Atlantic Coast Comprehensive Study (NACCS) domain, defined from the United States–Canadian border in Maine to the Virginia–North Carolina border. The motivation of the regional wave modeling is to provide offshore boundary conditions for the simulation of extreme extratropical and synthetic tropical events to drive the nearshore wave and surge modeling efforts within the NACCS. The offshore wave conditions were estimated using the third-generation WAve Modelling (WAM) model. Value-added wind fields were defined for each of the four wave model grids (North Atlantic Ocean Basin, U.S. Coastal Regional scale, and two subregional-scale grid systems covering the NACCS coastal domain)...