•Climate oscillations influence the wave climate of the South Atlantic Ocean.•Southern Annular Mode index positively correlates to Hs and Tm along Brazilian Coast.•La Niña promotes a major entrance ...of North Atlantic swell in South Atlantic Ocean.•El Niño promotes waves associated to mid-latitude cyclones in South Atlantic Ocean.•Pacific Decadal Oscillation index negatively correlates to both Hs and Tm in SAO.
Modes of variability in ocean wave conditions are coupled to atmospheric circulation changes due to exchange of energy and momentum at the interface. Here, we explored for the South Atlantic Ocean the relations between three main climate oscillations (El Niño–Southern Oscillation ENSO, Southern Annular Mode SAM, and Pacific Decadal Oscillation PDO), four wave parameters (significant wave height Hs, mean wave period Tm, and zonal Dm,x and meridional Dm,y wave direction components) and wind parameters (wind speed WS10, and zonal u10 and meridional v10 components). For this purpose, we regressed wind and wave parameters against the oscillation indices to create spatial composites of slope values, quantifying the correlation between wave parameters and indices. An EOF (empirical orthogonal function) analysis was also carried out to identify variability modes of wave parameters and to associate them to each climate index. The combining effects of ENSO and SAM were analysed by calculating Hs, Tm and wind speed anomalies for the periods in which the phases of these oscillations co-occur. We found important correlations not only with the dominant mode of variability, but also with secondary and even quaternary modes. For ENSO, negative correlations between the Oceanic Niño Index (ONI) and Hs, Tm, and Dm,x in the northwest part of the South Atlantic Ocean were highlighted, with a decrease (increase) of up to 8 cm of Hs per ONI unit in El Niño (La Niña) events. We established positive correlations also between ONI and these wave parameters in subtropical regions along the western African coast during austral summer, which were intensified by negative SAM. During autumn, however, we observed La Niña positive Hs anomalies for this region, which were also intensified by negative SAM. Finally, we found new, significant correlations between South Atlantic Ocean wave climate and SAM. We determined that the PDO index has negative correlations with Hs and Tm, while directional components present stronger variability.
We present a high resolution analysis of the interaction of irregular waves with natural and urban structures leading to extreme wave runup. Horizontal runup data, instantaneous flooding maps, and ...wave propagation beyond the coastline are numerically predicted. The novel methodology combining the Wave Watch III, SWAN and SWASH models to achieve accurate and computationally feasible simulation of waves at different time and spatial scales, from the formation process at deep water up to the total energy dissipation in the swash zone, is proposed. An access to the LIDAR database has provided a high resolution (15cm–25cm) of the subaerial surface which is essential for accurate representation of the hydrodynamic interactions with the beach profile. The suggested approach has been applied for evaluation of wave runup related to six storm events in Tramandaí Beach in Southern Brazil. This allowed for an identification of critical vulnerable overwashing areas as well as, critical information on flooding zones. The results are in agreement with the runup measurements performed in January 2014. The numerical methodology employed in this work has been also compared with the survey and conventional empirical model data. It was discovered that the empirical models lead to the systematic overestimation of the runup results.
We present a new deep learning training framework for forecasting significant wave height on the Southwestern Atlantic Ocean. We use the long short-term memory algorithm (LSTM), trained with the ERA5 ...dataset and also with buoy data. The forecasts are made for seven different locations in the Brazilian coast, where buoy data are available. We consider four different lead times, e.g., 6, 12, 18 and 24 h. Experiments are conducted using exclusively historical series at the selected locations. The influence of other variables as inputs for training is investigated. Results of the LSTM forecast show that a data-driven methodology can be used as a surrogate to the computational expensive physical models and also as an alternative to the reanalysis data. Accuracy of the forecasted significant wave height is close to 87% when compared to real buoy data.
•Long short-term memory is used to predict significant wave height.•Training is performed using reanalysis data and real observations.•Analysis conducted with historical series at seven locations in the Brazilian coast.•The influence of other variables as inputs for training is investigated.
The forecast of wave variables are important for several applications that depend on a better description of the ocean state. Due to the chaotic behaviour of the differential equations which model ...this problem, a well know strategy to overcome the difficulties is basically to run several simulations, by for instance, varying the initial condition, and averaging the result of each of these, creating an ensemble. Moreover, in the last few years, considering the amount of available data and the computational power increase, machine learning algorithms have been applied as surrogate to traditional numerical models, yielding comparative or better results. In this work, we present a methodology to create an ensemble of different artificial neural networks architectures, namely, MLP, RNN, LSTM, CNN and a hybrid CNN–LSTM, which aims to predict significant wave height on six different locations in the Brazilian coast. The networks are trained using NOAA’s numerical reforecast data and target the residual between observational data and the numerical model output. A new strategy to create the training and target datasets is demonstrated. Results show that our framework is capable of producing high efficient forecast, with an average accuracy of 80%, that can achieve up to 88% in the best case scenario, which means 5% reduction in error metrics if compared to NOAA’s numerical model, and a increasingly reduction of computational cost.
•An ensemble of neural networks is proposed to predict significant wave height.•A new framework to construct the training dataset is established.•The methodology is compared to traditional numerical models.•The framework is capable of producing high accuracy forecasts.
The shortage of observational ocean wave data in the South Atlantic (SA) Ocean has resulted in numerical modelling becoming the most used tool for the investigation of wave climate in this oceanic ...region. In this article, the global model WAve Model (WAM) is used to simulate ocean waves in the SA from June 2006 to July 2007 with high time resolution. The four leading modes of the significant wave height, swell, wave peak period and surface wind velocity based on the empirical orthogonal functions (EOF) and singular value decomposition (SVD) methods are computed and analysed. The results show a number of specific characteristics present in the short-scale regime which emphasise and, in some cases, reduce some of the aspects of the global wave climate. The interaction between atmosphere and ocean has been found in several fields and modes that were examined. A relationship between tracks of extratropical cyclones are observed.
Little research has been undertaken into sediment dynamics in lakes, and most of it only analyses particular aspects such as the texture of the sediments. In this study, the characteristics of the ...wave field in Guaíba Lake are investigated. The parameters significant wave height (Hs), period (T) and direction of wave propagation are examined together with their relation to the resuspension of sediments at the bottom. For this purpose, the mathematical model SWAN (Simulating Waves Nearshore) has been validated and employed. The results pointed out that the highest waves modeled reached 0.55 m at a few points in the lake, particularly when winds were blowing from the S and SE quadrants with an intensity over 7 m.s-1. Generally speaking, waves follow wind intensity and direction patterns, and reach maximum height in about 1 to 2 hours after wind speed peaks. Whenever winds were stronger, waves took some 2 hours to reach 0.10 m. However, with weak to moderate winds, the waves took around 3 hours to achieve this value in significant wave height. In addition to speed and direction, wind regularity proved relevant in generating and propagating waves on Lake Guaíba. In conclusion the lake's sediment environments were mapped and classified as follows: 1) Depositional Environments (51% of the lake); 2) Transitional Environments (41%); and 3) Erosional or Non-Depositional Environments (8%). As a contribution to the region's environmental management, elements have been created relating to the concentration of suspended particulate matter.
Pesquisas referentes à dinâmica sedimentar em lagos são escassas e a maioria trata da distribuição e textura dos sedimentos, sendo raras aquelas que fazem menção ao padrão de ondas e suas relações com a ressuspensão destes sedimentos e suas consequências. Este trabalho analisa as características das ondas incidentes no Lago Guaíba (Brasil) por meio da utilização do SWAN (Simulating Waves Nearshore) e suas relações com a ressuspensão de sedimentos junto ao fundo. Os resultados mostraram que as maiores ondas incidentes atingiram 0.55 m, particularmente quando de ventos do quadrante S e SE e com velocidades maiores que 7 m/s. Em termos gerais, as características das ondas seguem os padrões de intensidade e direção dos ventos, atingindo seus máximos valores aproximadamente 1 ou 2 horas após a velocidade de pico dos ventos. Em conclusão, os ambientes de sedimentação do lago foram mapeados e classificados da seguinte forma: 1) Ambientes Depositionais (51% da área do lago); 2) Ambientes Transicionais (41%); e 3) Ambientes Erosionais ou de não deposição (8%).Como forma de contribuir à gestão ambiental da região, foram gerados subsídios referentes ao potencial de concentração de material particulado em suspensão.
A mathematical model for the one-dimensional mass transport describing the concentration evolution of suspended sediments over a viscoelastic mud layer in the presence of erosion is presented. Using ...a perturbation method, the problem is set in terms of the concentration of particles at the water–mud interface. Numerical results show considerable difference from the cases of rigid and non-erodible interfaces. A singular behaviour of the particles concentration is observed when the mud depth approaches a resonant value, associated with negative convection velocity.
General propagation patterns due to the multiple interaction of waves and seamounts located in Northeast Brazil are investigated using SWAN. After conducting a model sensitivity test using in situ ...data, four realistic cases (in which the maximum period of the incoming swells was 17.6s) and one synthetic case (using the maximum period as 21.3s) have been simulated. The incoming wave periods and the minimum seamount depth determine whether the interaction happens or not. When the interaction occurs, changes in wave direction, significant wave height (Hs), and shadow zone length increases with higher incoming wave periods. Shoaling and wave refraction occur concomitantly, leading to lower significant wave heights on the flanks of the seamounts and higher values of this variable over the shallowest parts. The observed wave alterations are also due to the topographic orientation of the seamounts’ shallower portion relative to the incoming wave direction and to the interaction between waves affected by more than one seamount. For the shallowest seamount in the study area (minimum depth of 21m), the maximum significant wave height increased 43.7% and 25.3% relative to the incoming wave heights for the synthetic and realistic cases, respectively.
•A set of shallow seamounts, known as Ceará seamounts, prone to affect wave propagation, are found in Northeast Brazil.•The multiple interaction of waves with real seamounts is analyzed by numerical tools.•Shallow seamounts affect wave height and direction, resembling deep water lenses.•The seamount's orientation relative to the incident wave direction plays an important role in the resulting wave fields.
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