Wind wave footprint of tropical cyclones from satellite data

ABSTRACT: Tropical cyclones are associated with extreme winds, waves, and storm surge, being among most destructive natural phenomena. Developing capability for a rapid impact estimate is crucial for coastal applications and risk preparedness. When predicting waves characteristics associated to tropical cyclones, the traditional approach involves a two-step procedure (a) a Holland-type wind vortex model and (b) numerical simulations using a wave generation model, using buoy and satellite measurements for validation. In this work, we take advantage of the increasing amount of remote sensing observational data and propose a new empirical model to estimate the wind wave footprint of tropical cyclones. For this purpose, we construct a dataset with over a million satellite observations of waves triggered by tropical cyclones assuming a circular shape of the TC influence area and defining composites of significant wave height as a function of representative parameters of the track characteristics like the minimum pressure, its forward velocity, and its latitude. The validation against buoy data confirms the usefulness of the model for a first and rapid estimation of the wave footprint, although an underestimation of the most extreme events is observed due to the relatively small number of observations recorded. Due to its efficiency, the model can be applied for rapid estimations of wave footprints in operational systems, reconstruction of historical or synthetic events and risk assessments.This work would not have been possible without funding from the Strategic Environmental Research and Development Program's grant DOD/SERDP RC-2644 and from the Spanish Ministry of Science and Innovation, project Beach4cast PID2019-107053RB-I00. Ana Rueda funded by a Juan de la Cierva Incorporación Scholarship (IJC2020-04390). Laura Cagigal is funded by a scholarship from the University of Auckland. Open access publishing facilitated by The University of Auckland, as part of the Wiley - The University of Auckland agreement via the Council of Australian University Librarians

The application of ensemble wave forcing to quantify uncertainty of shoreline change predictions.

ABSTRACT: Reliable predictions and accompanying uncertainty estimates of coastal evolution on decadal to centennial time scales are increasingly sought. So far, most coastal change projections rely on a single, deterministic realization of the unknown future wave climate, often derived from a global climate model. Yet, deterministic projections do not account for the stochastic nature of future wave conditions across a variety of temporal scales (e.g., daily, weekly, seasonally, and interannually). Here, we present an ensemble Kalman filter shoreline change model to predict coastal erosion and uncertainty due to waves at a variety of time scales. We compare shoreline change projections, simulated with and without ensemble wave forcing conditions by applying ensemble wave time series produced by a computationally efficient statistical downscaling method. We demonstrate a sizable (site-dependent) increase in model uncertainty compared with the unrealistic case of model projections based on a single, deterministic realization (e.g., a single time series) of the wave forcing. We support model-derived uncertainty estimates with a novel mathematical analysis of ensembles of idealized process models. Here, the developed ensemble modeling approach is applied to a well-monitored beach in Tairua, New Zealand. However, the model and uncertainty quantification techniques derived here are generally applicable to a variety of coastal settings around the world

Climate-Based Emulator of Distant Swell Trains and Local Seas Approaching a Pacific Atoll

ABSTRACT: Wave-induced flooding is a major coastal hazard for the low-lying atolls of the Pacific. These flooding events are expected to increase over time, which may cause significant coastal damage in some locations. Coastal flooding analysis (forensic or forecasted) is particularly challenging in these small islands due to the co-occurrence of several swells and local seas propagating in a complex configuration of archipelagos. Therefore, assessing the contribution of swells and wind seas on the flooding hazards that threaten the atoll islands requires the spectral characterization of the wave climate, since integrated wave parameters do not accurately represent the wave conditions in these environments. On the other hand, the relative short records of wave conditions, represent only a small fraction of the possible range of combinations that could produce a wave-induced flooding event. For these reasons, we propose the analysis of all the spectral energy arriving toward a study site, by isolating and parameterizing each swell train. Then, taking into account the link with large-scale climatic patterns (i.e., El Niño Southern Oscillation), we present a new multi-modal seas emulator capable of generating infinitely long time series of synthetic individual swell trains and seas. This new climate-based emulator allows a better understanding of swell behavior in the Pacific, and the generation of multimodal wave conditions to populate the historical records as a key point to perform robust coastal flood risk assessments considering climate variability

A Multiscale Approach to Shoreline Prediction

ABSTRACT: Shorelines respond to a number of "drivers" operating on a variety of time-scales. For some time-scales (e.g., seasonal), the driver-shoreline relationship is often evident; however, at longer timescales (e.g., multiannual), the shoreline changes may be superimposed on changes at shorter time-scales and thus are diffcult to identify. Here, we predict shoreline evolution from storm events to decadal timescales, using a novel approach based on the Complete Ensemble Empirical Mode Decomposition. This approach identifies and links the primary time-scales in the model drivers (large-scale sea level pressure [SLP] and/or waves) with the same time-scales in the shoreline position. The multiscale approach reproduced shoreline changes at two beaches more skillfully than a common shoreline model when SLP and wave information were used in combination. In addition, the analysis can be applied to climate indices, providing the opportunity to link longer time-scales with climate patterns (e.g., El Niño Southern Oscillation)

Controls of Multimodal Wave Conditions in a Complex Coastal Setting

Coastal hazards emerge from the combined effect of wave conditions and sea level anomalies associated with storms or low-frequency atmosphere-ocean oscillations. Rigorous characterization of wave climate is limited by the availability of spectral wave observations, the computational cost of dynamical simulations, and the ability to link wave-generating atmospheric patterns with coastal conditions. We present a hybrid statistical-dynamical approach to simulating nearshore wave climate in complex coastal settings, demonstrated in the Southern California Bight, where waves arriving from distant, disparate locations are refracted over complex bathymetry and shadowed by offshore islands. Contributions of wave families and large-scale atmospheric drivers to nearshore wave energy flux are analyzed. Results highlight the variability of influences controlling wave conditions along neighboring coastlines. The universal method demonstrated here can be applied to complex coastal settings worldwide, facilitating analysis of the effects of climate change on nearshore wave climate.This work was funded by the U.S. Geological Survey (USGS) Coastal and Marine Geology Program. The authors thank Jorge Perez, IH Cantabria, for providing the GOW wave hindcast and for assistance with wave spectra, and Sean Vitousek, University of Chicago, for a helpful review. This material is based upon work supported by the U.S. Geological Survey under grant/cooperative agreement GI5AC00426. A. R., J. A. A. A., and F. J. M. acknowledge the support of the Spanish “Ministerio de Economía y Competitividad” under grant BIA2014-59643-R. J. A. A. A. was funded by the Spanish “Ministerio de Educación, Cultura y Deporte” FPU (Formación del Profesorado Universitario) studentship BOE-A-2013-12235. Reanalyses of ocean data are available for research purposes through IH Cantabria (contact [email protected]). Southern California Bight look-up table data are available at https://doi.org/10.1594/PANGAEA.880314. Related Southern California nearshore wave data can be found at http://dx.doi.org/10.5066/F7N29V2V

Un modelo empírico para la estimación del oleaje producido por ciclones tropicales a partir de datos de satélite

Se agradece la financiación del proyecto BIA2014-59643-R del Ministerio de Economía y Competitividad

Análisis probabilístico de la evolución de la línea de costa

LC agradece la financiación de su beca de doctorado en la Universidad de Auckland. GC, FM, JM y AR por la financiación del GNS-Hazard Platform Project

Projecting Climate Dependent Coastal Flood Risk With a Hybrid Statistical Dynamical Model

ABSTRACT: Numerical models for tides, storm surge, and wave runup have demonstrated ability to accurately define spatially varying flood surfaces. However these models are typically too computationally expensive to dynamically simulate the full parameter space of future oceanographic, atmospheric, and hydrologic conditions that will constructively compound in the nearshore to cause both extreme event and nuisance flooding during the 21st century. A surrogate modeling framework of waves, winds, and tides is developed in this study to efficiently predict spatially varying nearshore and estuarine water levels contingent on any combination of offshore forcing conditions. The surrogate models are coupled with a time-dependent stochastic climate emulator that provides efficient downscaling for hypothetical iterations of offshore conditions. Together, the hybrid statistical-dynamical framework can assess present day and future coastal flood risk, including the chronological characteristics of individual flood and wave-induced dune overtopping events and their changes into the future. The framework is demonstrated at Naval Base Coronado in San Diego, CA, utilizing the regional Coastal Storm Modeling System (CoSMoS; composed of Delft3D and XBeach) as the dynamic simulator and Gaussian process regression as the surrogate modeling tool. Validation of the framework uses both in-situ tide gauge observations within San Diego Bay, and a nearshore cross-shore array deployment of pressure sensors in the open beach surf zone. The framework reveals the relative influence of large-scale climate variability on future coastal flood resilience metrics relevant to the management of an open coast artificial berm, as well as the stochastic nature of future total water levels.This work was funded by the Strategic Environmental Research Development Program (DOD/SERDP RC-2644). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. F. J. Mendez, A. Rueda, and L. Cagigal acknowledge the partial funding from the Spanish Ministry of Science and Innovation, project Beach4cast PID2019-107053RB-I00. The authors thank the Scripps Center for Coastal Studies for their efforts to deploy, recover, and process surf zone pressure sensor data used as validation in this study. The authors thank Melisa Menendez for sharing GOW2 hindcast data for Southern California. The authors thank the sea-level rise projection authors for developing and making the sea-level rise projections available, multiple funding agencies for supporting the development of the projections, and the NASA Sea-Level Change Team for developing and hosting the IPCC AR6 Sea-Level Projection Tool

Tesla: Un emulador multi-escala de eventos de inundación costera

Se agradece la financiación parcial del proyecto “SISTEMA INTEGRADO DE PREDICCIÓN PROBABILÍSTICA DE INUNDACIÓN Y EROSIÓN EN PLAYAS (SODERCAN/FEDER)”