Tropical Cyclone Wave Research Articles

Study of tropical cyclone wave characteristics based on a hybrid track clustering method

Study of tropical cyclone wave characteristics based on a hybrid track clustering method

Tropical cyclone wave data assimilation impact on air-ocean-wave coupled Hurricane Harvey (2017) forecast

The impact of surface wave assimilation on hurricane track and intensity forecasts has been investigated using a fully coupled air-ocean-wave tropical cyclone data assimilation and forecast modeling system. A new 3DVAR wave assimilation method in the Navy Coupled Ocean Data Assimilation system (NCODA) maps the 1D wave energy spectra from buoys to 2D directional wave energy spectra using the maximum likelihood method (MLM) and corrects the wave model forecast component directional wave energy spectra. The Coupled Ocean/Atmosphere Mesoscale Prediction System for Tropical Cyclone Prediction (COAMPS-TC) is used to conduct three Hurricane Harvey (2017) air-ocean-wave coupled data assimilation and forecasting experiments with and without the wave data assimilation. Hurricane Harvey traversed through the Western Gulf of Mexico from 24 August to 1 September, 2017 and made landfall in the Texas and Louisiana coast. Validation of track, maximum wind speed, significant wave height, and mean absolute wave periods show wave assimilation of the 1D wave energy spectra from 13 National Data Buoy Center (NDBC) buoys reduced the forecast errors of these parameters compared to experiments without the wave assimilation. In spite of this positive outcome, the wave assimilation is unable to reduce Harvey’s 0-120 h forecast mean wave direction errors and correlation compared to the NDBC buoy time series

THE PHYSICAL PROCESSES ACTIVE IN TROPOCAL CYCLONE WAVE GENERATION

In tropical and subtropical regions tropical cyclones represent the major extreme meteorological events, generating winds in excess of 40m/s and significant wave heights above 10m. Such systems are characterized by a well-formed translating vortex wind field with a calm eye and winds that spiral in toward the center of the storm. Despite the apparent complexity of the forcing wind field, observations of waves in tropical cyclones show that there are many similarities to waves generated in relatively simple cases with approximately constant unidirectional winds (Tamizi and Young 2020. It has often been speculated (Tamizi and Young 2020) that this is because nonlinear wave–wave interactions play a dominate role in defining the tropical cyclone directional wave spectrum and hence the significant wave height, as they do for more simple wave generation cases. Despite this speculation and the many observational studies of tropical cyclone wave spectra, no study has yet been able to demonstrate the role of nonlinear wave–wave interactions in tropical cyclones. Firstly, the observational database from buoys and remote sensing systems are summarized. A third generation spectral wave model is then used to model typical tropical cyclones and the role played by each of wind input, dissipation and nonlinear interactions is investigated in detail.

Development and validation of a parametric tropical cyclone wave height prediction model

A parametric model for computationally efficient wave height estimation within tropical cyclone conditions is developed. An updated wind vortex model is used to force a moving grid version of the WAVEWATCH III spectral model, to generate a large synthetic tropical cyclone wave field database (approximately 400 simulations). This extensive database is used to develop the parametric model which accounts for the key variables defining the magnitude and spatial distribution of waves within tropical cyclones. The model uses JONSWAP-type scaling and accounts for the effects of the moving vortex of the tropical cyclone in defining the wave field. A total of 28 buoy observations together with satellite altimeters measurements during the passage of 17 tropical cyclones (hurricanes) in the North Atlantic Ocean and Gulf of Mexico were used to validate the predicted wave heights. The resulting model is computationally efficient, making it ideal for engineering applications which require simulation of numerous tropical cyclone cases. Despite some limitations, it is a useful tool for the prediction of wave heights under extreme tropical cyclone forcing.

A Numerical Simulation Study on the Probable Maximum Typhoon Wave in the South China Sea

The South China Sea spans the tropics and subtropics. Tropical cyclones in the area are extremely active, due to the features of its marine environment, such as wide water-depth profile, complex topography and hydrology. The maximum wave heights along the coast of China are normally generated by typhoons. Especially in the context of global warming, extreme weather events have significantly increased in the recent years, leading to more frequent strong and super typhoons. With the development of resources and energy in China expanding into the deep sea, extreme waves have caused serious damage to sea projects, endangering people’s lives and properties. Selecting the accurate typhoon gradient model to calculate various extreme waves, including the probable maximum tropical cyclone (PMTC) wave, is of significance for the safety of marine engineering construction and disaster prevention and mitigation. In this paper, we first proposed a wind field fusion model suitable for the South China Sea by superimposing an empirical typhoon model with the background wind field, and further verified it using the measured typhoon data. Secondly, the fused wind field was used as the input wind field of the SWAN (Simulating Waves Nearshore) model, and the wave fields of typhoons “Usagi” and “Mangosteen” were used to verify the model. The relevant parameters of PMTC were calculated using the Pearson Type III frequency fitting method, while the verified SWAN model was used to calculate the probable maximum typhoon wave, and P-III frequency analysis was carried out by direction of the extrapolated result of typhoon waves to obtain the design wave elements of each return period. Finally, a model for calculating the probable maximum typhoon wave suitable for this sea area was proposed to derive the characteristic parameters and time-histories of the probable maximum typhoon wave and the wave heights and their corresponding frequencies of various extreme waves.

Wave field predictions using a multi-layer perceptron and decision tree model based on physical principles: A case study at the Pearl River Estuary

In this study, a novel data-driven model is proposed for efficient spatial–temporal forecasting of the significant wave height (SWH), mean wave period (MWP), and mean wave direction (MWD) in the Pearl River Estuary, located in Southern China, using machine learning. The model utilizes machine learning techniques and employs a multi-layer perceptron (MLP) and decision tree (DT) based architecture following a matrix-to-matrix mapping strategy to establish the underlying spatial–temporal relationship between the wind and wave fields for four-day and ocean-scale forecasts. The training and testing of the model were carried out using long-term hindcast wave and wind datasets released by ERA5. A rectangular sea region covering an area of 5° × 5°in the Pearl River Estuary was regarded as the modeling domain. The data-driven model was validated against a 10 years of historical hindcast data, and the global errors were analyzed. The forecasting performance of the established model for tropical cyclone wave events was investigated in terms of both the spatial pattern and temporal variance of the SWH, MWP, and MWD. Extreme waves were generated using the proposed model and compared with the original hindcast results. The data-driven model showed promising potential for accurately capturing ambiguous patterns and features that are variant in both spatial and temporal dimensions, and exhibited obvious superiority over numerical wave modeling in terms of computational efficiency.

Whitecapping term analysis of extreme wind wave modelling considering spectral characteristics and water depth

The Simulating WAves Nearshore (SWAN) model can be used to simulate extreme wind waves such as tropical cyclone waves, which are of great significance for the study of coastal processes and the protection of coastal engineering. In this study, the basic setting and whitecapping term of the SWAN model are analysed to model tropical cyclone waves, with spectral characteristics and water depth taken into account. By investigating the temporal variation of bulk wave parameters and wave spectral characteristics of key nodes, the optimal values of 4 basic setting parameters are determined. By analysing the sensitivity of tuning effects of key coefficients in different whitecapping formulations, calibration criteria of these parameters are provided, and the coefficients Stpm and Powk in the Komen formulation are found to be most sensitive to the outputted significant wave height and mean wave period, respectively. To further study the sensitivity of these coefficients, especially their relationship with water depth, spatial analyses of sensitivity variations are performed in the coastal region, which show that the sensitivity of these coefficients increases first and then tends to be stable with the increase of water depth, indicating a critical water depth between 20 m and 50 m. This study provides insights for more effective and directional modelling of tropical cyclone waves in coastal and shelf seas.

Spatial-temporal wave height forecast using deep learning and public reanalysis dataset

A novel data-driven model is proposed for efficient spatial–temporal significant wave height forecast in West Pacific using deep learning. A multivariate 3-layer LSTM-based architecture following a matrix-to-matrix mapping fashion is applied to establish the underlying spatial–temporal relationship between wind components and significant wave height field for long-term and ocean-scale forecasts. A parallel modeling strategy is employed to reduce the computational expense in which the forecasting domain is flexibly partitioned into finite windows and trained independently as a sub-model. A 30-year hindcast wave dataset released by EMC (Environmental Modeling Center) Operational Wave Models associated with NCEP Climate Forecast System Reanalysis and Reforecast (CFSRR) wind data is used to drive and test the model. A rectangle sea region covering over 9.33 × 106 km2 in West Pacific is regarded as a modeling domain. The data-driven model is validated against a 12-year period historical hindcast data, and global errors are carefully discussed. The forecasting performance of the established model on tropical cyclone wave events is investigated in terms of both spatial pattern and temporal variance of significant wave height. The extreme significant wave height, rough sea occurrence, as well as seasonal and annual characteristics of significant wave height in the West Pacific are carried out using the proposed model and compared with the original hindcast results. The data-driven model shows promising potential to accurately capture the ambiguous patterns and features that are variant in both spatial and temporal dimensions and has obvious superiority over numerical wave modeling in terms of computational efficiency.

Coral Reef Exposure to Damaging Tropical Cyclone Waves in a Warming Climate

AbstractTropical cyclones generate large waves that physically damage coral communities and are commonly cited as a worsening threat to coral reefs under climate change. However, beyond projections of ocean basin‐scale changes in cyclone intensity, the other determinants of future coral reef damage such as cyclone size and duration remain uncertain. Here, we determine the extent to which downscaled cyclones represent observed cyclone characteristics that influence wave damage to Australian coral reef regions. We then investigate mid‐century (2040–2060) and end of century (2080–2100) downscaled tracks to assess whether cyclone characteristics will change with future warming under a high‐emissions scenario. We find that spatial uncertainties in downscaled cyclogenesis and track positions limit estimates of reef damage for individual coral reefs and regions. Further, the models are unable to reproduce the most reef damaging cyclones for any of the regions. The downscaled tracks capture observed cyclone characteristics, such as size, impacting the Great Barrier Reef well, but perform poorly for the Northern Territory, with mixed performance for the Coral Sea and Western Australia. We find no clear evidence that cyclones will cause more damage to Australian coral reef regions in the future, at least based on the climate models and downscaling approach examined here. There is increasing interest in using tropical cyclone projections to assess future coral reef exposure to damaging waves. We recommend caution when interpreting such projections due to large uncertainty in the mechanisms that influence reef damaging tropical cyclone characteristics and how these will change with future warming.

Development of a hybrid wind field for modeling the tropical cyclone wave field

Due to the increasing trend of tropical cyclone occurrence in the recent decades, the necessity of developing models for forecasting and hindcasting these atmospheric-oceanic hazards, becomes more apparent. One of the uncertainties about the tropical cyclones is the wind field accuracy of modeling oceanic waves and flows. In this paper, six different wind fields have been examined for hindcasting the wave field of cyclone Gonu within the areas of the Arabian Sea and the Gulf of Oman. The wind field of ECMWF (ERA-Interim) and Climate Forecast System Reanalysis (CFSR) data, that obtained via weather research forecasting (WRF) mesoscale modeling and three wind fields achieved via Holland, Rankin, Young and Sobey (S&Y) parametric models have been investigated as well. The wave field modeling results have been compared with the third-generation SWAN model in two stations within the area of Chabahar Bay. The wind field results obtained via the reanalysis models and WRF one indicate an appropriate estimation of the wave characteristics and the maximum wave height at the measurement stations. In order to better evaluate the results of different wind fields, the maximum wave height maps have been studied in the simulation time period. The results indicated that the maximum wave height values obtained from the reanalysis data do not match the track and development direction of the cyclone Gonu. The mismatch between storm development track and maximum wave height in WRF data can also be observed. The maximum significant wave height obtained via the parametric wind fields is very compatible with the cyclone Gonu track. Examining the maximum wave heights obtained via different wind fields, along with comparing the wave characteristics at the measurement stations, highlights the effect of different wind fields in hindcasting the wave characteristics, especially on the northern coasts of the Gulf of Oman. Examination of the maximum wave heights obtained from different wind fields has been investigated in this study along with comparison of wave characteristics at measurement stations. Results indicated that the optimal approach for simulating the tropical cyclone's wave field is to use a hybrid one. Therefore, a hybrid wind field has been developed which includes the wind characteristics of the parametric model in the areas near the storm source and the WRF meso scale model in the outer zone. The hybrid wind field has led to an improvement in the characteristics of waves within deep and coastal waters.

ATDNNS: An adaptive time–frequency decomposition neural network-based system for tropical cyclone wave height real-time forecasting

Accurate prediction of anomalous wave heights caused by tropical cyclones (TC) is extremely significant for coastal safety and offshore operations. Previous studies have barely considered TC wave height data which are characterized by short, nonlinear and non-stationary data, in this paper, an adaptive time–frequency decomposition method and a mixture of deep learning methods are used to develop a real-time TC wave height forecasting system (ATDNNS). Specifically, based on wave height data obtained from distributed sensors offshore China, the system uses ensemble empirical mode decomposition (EEMD) to decompose wave height data into multiple subseries containing local features of the original signal at different time scales, and these smoothed subseries are used as inputs to a long short-term memory network (LSTM) to reconstruct all outputs to provide accurate TC wave height forecasts. This study involves 28 TC events in the northwest Pacific Ocean with data collected from 14 coastal buoys over 9 years. The experimental results show that ATDNNS significantly outperforms the baseline model and previous studies. By comparing the results with the operational results of the state-of-the-art operational numerical models, it is shown that the system has the potential to fill the gap of poor initial forecast performance of numerical models, and we further check the effectiveness of ATDNNS for wave heights caused by the recent super typhoon Kong-Ray, achieving excellent results.

Future Changes in Tropical Cyclone and Easterly Wave Characteristics over Tropical North America

Tropical Cyclones (TCs) and Easterly Waves (EWs) are the most important phenomena in Tropical North America. Thus, examining their future changes is crucial for adaptation and mitigation strategies. The Community Earth System Model drove a three-member regional model multi-physics ensemble under the Representative Concentration Pathways 8.5 emission scenario for creating four future scenarios (2020–2030, 2030–2040, 2050–2060, 2080–2090). These future climate runs were analyzed to determine changes in EW and TC features: rainfall, track density, contribution to seasonal rainfall, and tropical cyclogenesis. Our study reveals that a mean increase of at least 40% in the mean annual TC precipitation is projected over northern Mexico and southwestern USA. Slight positive changes in EW track density are projected southwards 10° N over the North Atlantic Ocean for the 2050–2060 and 2080–2090 periods. Over the Eastern Pacific Ocean, a mean increment in the EW activity is projected westwards across the future decades. Furthermore, a mean reduction by up to 60% of EW rainfall, mainly over the Caribbean region, Gulf of Mexico, and central-southern Mexico, is projected for the future decades. Tropical cyclogenesis over both basins slightly changes in future scenarios (not significant). We concluded that these variations could have significant impacts on regional precipitation.

The physics of ocean wave evolution within tropical cyclones

AbstractA series of numerical experiments with the WAVEWATCH III spectral wave model are used to investigate the physics of wave evolution in tropical cyclones. Buoy observations show that tropical cyclone wave spectra are directionally skewed with a continuum of energy between locally generated wind-sea and remotely generated waves. These systems are often separated by more than 900. The model spectra are consistent with the observed buoy data and are shown to be governed by nonlinear wave-wave interactions which result in a cascade of energy from the wind-sea to the remotely generated spectral peak. The peak waves act in a “parasitic” manner taking energy from the wind-sea to maintain their growth. The critical role of nonlinear processes explains why one-dimensional tropical cyclone spectra have characteristics very similar to fetch-limited waves, even though the generation system is far more complex. The results also provide strong validation of the critical role nonlinear interactions play in wind-wave evolution.

Assessment of tropical cyclone wave models for engineering applications

Tropical Cyclone (TC) wind and wave models are used to quantify meteorological and oceanographic conditions in the application of offshore engineering design criteria. In regions such as the North West Shelf of Australia, a statistically sound 'risk of failure' design assessment can require historical records far longer than are typically available, even with meteorological datasets of several decades length. To address this mismatch between design requirements and observational history, synthetic tropical cyclone wind and wave datasets can be developed to mimic long records with the objective of estimating extreme event average recurrence intervals of 10,000 years. Modelling large numbers of TC storms in practice requires a trade-off between time (computational efficiency) and accuracy (model skill). The development of the synthetic dataset in this study draws the balance by combining computationally efficient parametric models, and computationally intensive fully dynamic models, with different model grid resolutions. This paper outlines performance differences between the various model approaches and make recommendations for engineering.

A TROPICAL CYCLONE WIND, WAVE AND STORM TIDE RISK DESIGN AND WARNING TOOLBOX FOR AUSTRALIA

The paper will describe the development and operation of a comprehensive tropical cyclone wind, wave and storm tide risk design and warning software toolbox that covers all of tropical-influenced Australia, The toolbox is designed to provide both rapid assessment and detailed design capabilities for coastal, port, offshore and nearshore design, including projected future climate change. It also provides real-time emergency management functionality. The toolbox provides a probabilistic design framework that facilitates the essential need for sensitivity analysis of both inputs and outputs without pre-conceived risk thresholds. It provides the essential hazard component in a robust and verified context that can then, depending on the application, enable more focused and efficient deterministic modelling stages using models of choice.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/pssgiYdkEbM

Sensitivity analysis of the numerical aspect of the SWAN for the tropical cyclone wave simulations in the Gulf of Oman

The simulating waves nearshore (SWAN) model is designed for the wave simulations of nearshore areas. In the present study, this model has been applied for simulating tropical cyclone–generated waves in the Gulf of Oman. The wind and pressure field of the Gonu cyclone were reproduced by means of the cyclone wind generation (CWG) tool of the Mike DHI within multiple inputs, including the geographical location of the center of the cyclone, the radius of maximum wind speed, the pressure at the center of the cyclone, the neutral pressure, the start time of the cyclone period, and the maximum wind speed. The required specifications of the cyclone were obtained from the Joint US Typhoon Warning Center (JTWC) and introduced into the CWG module. With the purpose of achieving optimal settings of the model, a sensitivity analysis of the time step, grid resolution, and angle resolution has been conducted. Sensitivity analysis demonstrates that effects of time step have a significant impact on the results, while generally speaking angel and grid resolution are less impressive.

Simulating tropical cyclone waves in the East China Sea with an event-based, parametric-adjusted model

Parametric cyclone wind models with storm characteristics derived from statistical representation do not agreeably delineate the wind field in the East China Sea, leading to uncertain errors in the numerical simulations of Typhoon waves. A new approach to adjust the cyclone wind field in the framework of the Holland model is proposed. The radius of maximum wind speed and the logarithmically varied shape parameter B from the Holland model are iterated out with in situ data and extended to the cyclone track in the East China Sea. This event-based Holland parametric assimilation scheme can model different kinds of unimodal wind profiles and deliver a significant improvement over the original Holland model. The simulated wind fields of three typhoon events, Typhoons Saomai (200608), Fitow (201312) and Maria (201808), are then used to drive the Simulating Waves Nearshore (SWAN) wave model, and wave fields during the three typhoon events show better agreement with the in situ observations. Compared with satellite scatterometer and altimeter products, the effectiveness of modelled typhoon events is largely limited by temporal changes of typhoon structure. This approach is recommended for typhoon events with small temporal changes. Overall, the approach to adjusting wind profiles with observations satisfactorily improves the simulation performances of cyclonic winds and wave heights at a low computation cost.

Near-reef and nearshore tropical cyclone wave climate in the Great Barrier Reef with and without reef structure

The Great Barrier Reef (GBR) coral coverage is in rapid decline from severe and sustained pressures from lagoon water quality, crown-of-thorns starfish (COTS), coral bleaching, tropical cyclones, pollution and diseases. The two recent GBR coral bleaching events (2016–2017) lead to Great Barrier Reef Marine Park Authority (GBRMPA) shifting their focus from passive management to active intervention (Great Barrier Reef Blueprint for resilience by GBRMPA). These active interventions, potentially able to increase GBR resilience, as there are reefs that, due to their physical location relative to all other reefs, river and estuary entrances, ocean currents, have favourable coral growth conditions. To undertake such interventions, various information is required including tropical cyclone wave climates. This paper develops tropical cyclone wave climates for the entire GBR. These wave climates were developed by simulating several thousand synthetic cyclones derived from the “HadGEM” general circulation model with RCP8.5 climate change scenario. The synthetic cyclones adopted herein include the following climate changes assessed by comparing averages of key forcing parameters between 1950 to 1999 and 2050 to 2099. Their average arrival rate increases from 2.25 to 2.41 cyclones/year and their average maximum wind speed increases from 24 to 28 m/s. Their average radius to maximum winds remains constant at 51 km. Two key challenges were resolved, namely, long runtimes and large files (600 m grid increment covering 1800 km by 280 km). Runtimes were reduced by excluding cyclones where their wind speeds over the entire event never exceeded 10 m/s within GBR itself or within 100 km of the GBR over water. Maximum wave heights were compared with an extended fetch empirical expression, which was based on satellite data of tropical cyclones in open waters, when cyclones were outside the GBR lagoon. These comparisons indicate that predicted wave heights have a lower bias using default wave generation parameters when compared with the extended fetch empirical expression. Prediction uncertainty was estimated at no more than 10% from various cyclonic wind-field models. The existing GBR reefs reduce nearshore wave or runup height by between 1.5 and 2 times compared to the no reef case. The reduction in wave or runup height was found to be minimal for 1 m sea level rise. These two findings indicate that there is more flooding potential from coral removal than SLR within the GBR lagoon.

Extreme significant wave height of tropical cyclone waves in the South China Sea

Abstract. Extreme significant wave heights are assessed in the South China Sea (SCS), as assessments of wave heights are crucial for coastal and offshore engineering. Two significant factors include the initial database and assessment method. The initial database is a basis for assessment, and the assessment method is used to extrapolate appropriate return-significant wave heights during a given period. In this study, a 40-year (1975–2014) hindcast of tropical cyclone waves is used to analyse the extreme significant wave height, employing the peak over threshold (POT) method with the generalized Pareto distribution (GPD) model. The peak exceedances over a sufficiently large value (i.e. threshold) are fitted; thus, the return-significant wave heights are highly dependent on the threshold. To determine a suitable threshold, the sensitivity of return-significant wave heights and the characteristics of tropical cyclone waves are studied. The sample distribution presents a separation that distinguishes the high sample from the low sample, and this separation is within the stable threshold range. Because the variation in return-significant wave heights in this range is generally small and the separation is objectively determined by the track and intensity of the tropical cyclone, the separation is selected as a suitable threshold for extracting the extreme sample in the tropical cyclone wave. The asymptotic tail approximation and estimation uncertainty show that the selection is reasonable.

An automated threshold selection method based on the characteristic of extrapolated significant wave heights

The reasonable estimation of design wave heights is crucial for coastal and offshore engineering practices. To obtain sufficient data for model estimation, the peak over threshold (POT) method with the generalized Pareto distribution (GPD) model is employed. When using this method (i.e., the POT/GPD method), peak excesses over a sufficiently large value (i.e., threshold) are fitted. The extrapolated significant wave heights are highly dependent on the threshold, which must carefully be determined. In this study, significant wave heights from a 40-year (1975–2014) hindcast of tropical cyclone waves in the South China Sea (SCS) are adopted as the initial dataset to study extreme significant wave heights. Based on this initial dataset, the threshold selection is studied by analysing the influence of the excluded samples on the return significant wave heights when the threshold continuously increases. A stable threshold range corresponding to stable return significant wave heights is found, which exhibits a high probability of containing the suitable threshold. To determine the suitable threshold within the stable threshold range, an automated threshold selection method based on the characteristic of extrapolated significant wave heights (ATSME) is proposed. As shown by the reliability (via asymptotic tail approximation and estimation uncertainty) of the POT/GPD method and comparison with other threshold selection methods (i.e., the GPD parameter plot and automated threshold selection method based on the characteristic of the modified scale parameter (ATSMP)), the thresholds obtained by the ATSME are reasonable. Considering that the proposed method is based on the characteristic of extrapolated significant wave heights, data (such as measured, simulated and calculated data) participating in the extrapolation must carefully be processed, which influence the extrapolated results.