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Jose Henrique G. M. Alves and Michael L. Banner

Abstract

A new formulation of the spectral dissipation source term S ds for wind-wave modeling applications is investigated. This new form of S ds is based on a threshold behavior of deep-water wave-breaking onset associated with nonlinear wave-group modulation. It is expressed in terms of the azimuth-integrated spectral saturation, resulting in a nonlinear dependence of dissipation rates on the local wave spectrum. Validation of the saturation-based S ds is made against wave field parameters derived from observations of fetch-limited wind-wave evolution. Simulations of fetch-limited growth are made with a numerical model featuring an exact nonlinear form of the wave–wave-interactions source term S nl. For reference, the performance of this saturation-based S ds is compared with the performance of the wave-dissipation source-term parameterization prescribed for the Wave Modeling Project (WAM) wind-wave model. Calculations of integral spectral parameters using the saturation-based model for S ds agree closely with fetch-limited observations. It is also shown that the saturation-based S ds can be readily adjusted to accommodate several commonly used parameterizations of the wind input source term S in. Also, this new form of S ds provides greater flexibility in controlling the shape of the wave spectrum in the short gravity-wave region.

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Ali Tamizi, Ian R. Young, Agustinus Ribal, and Jose-Henrique Alves

Abstract

A very large database containing 24 years of scatterometer passes is analyzed to investigate the surface wind fields within tropical cyclones. The analysis confirms the left–right asymmetry of the wind field with the strongest winds directly to the right of the tropical cyclone center (Northern Hemisphere). At values greater than 2 times the radius to maximum winds, the asymmetry is approximately equal to the storm velocity of forward movement. Observed wind inflow angle (i.e., storm motion not subtracted) is shown to vary both radially and azimuthally within the tropical cyclone. The smallest observed wind inflow angles are found in the left-front quadrant with the largest values in the right-rear quadrant. As the velocity of forward movement increases and the central pressure decreases, observed inflow angles ahead of the storm decrease and those behind the storm increase. In the right-rear quadrant, the observed inflow angle increases with radius from the storm center. In all other quadrants, the observed inflow angle is approximately constant as a function of radial distance.

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Jose-Henrique G. M. Alves, Arun Chawla, Hendrik L. Tolman, David Schwab, Gregory Lang, and Greg Mann

Abstract

The development of a Great Lakes wave forecasting system at NOAA’s National Centers for Environmental Prediction (NCEP) is described. The system is an implementation of the WAVEWATCH III model, forced with atmospheric data from NCEP’s regional Weather Research and Forecasting (WRF) Model [the North American Mesoscale Model (NAM)] and the National Digital Forecast Database (NDFD). Reviews are made of previous Great Lakes wave modeling efforts. The development history of NCEP’s Great Lakes wave forecasting system is presented. A performance assessment is made of model wind speeds, as well as wave heights and periods, relative to National Data Buoy Center (NDBC) measurements. Performance comparisons are made relative to NOAA’s Great Lakes Environmental Research Laboratory (GLERL) wave prediction system. Results show that 1- and 2-day forecasts from NCEP have good skill in predicting wave heights and periods. NCEP’s system provides a better representation of measured wave periods, relative to the GLERL model in most conditions. Wave heights during storms, however, are consistently underestimated by NCEP’s current operational system, whereas the GLERL model provides close agreement with observations. Research efforts to develop new wave-growth parameterizations and overcome this limitation have led to upgrades to the WAVEWATCH III model, scheduled to become operational at NCEP in 2013. Results are presented from numerical experiments made with the new wave-model physics, showing significant improvements to the skill of NCEP’s Great Lakes wave forecasting system in predicting storm wave heights.

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Jose-Henrique G. M. Alves, Scott Stripling, Arun Chawla, Hendrik Tolman, and Andre van der Westhuysen

Abstract

Waves generated during Hurricane Sandy (October 2012) contributed significantly to life and property losses along the eastern U.S. seaboard. Extreme waves generated by Sandy propagated inland riding high water levels, causing direct destruction of property and infrastructure. High waves also contributed to the observed record-breaking storm surges. Operational wave-model guidance provided by the U.S. National Weather Service, via numerical model predictions made at NOAA’s National Centers for Environmental Prediction (NCEP), gave decision makers accurate information that helped mitigate the severity of this historical event. The present study provides a comprehensive performance assessment of operational models used by NCEP during Hurricane Sandy, and makes a brief review of reports issued by government agencies, private industry, and universities, indicating the importance of the interplay of waves and surges during the hurricane. Performance of wave models is assessed through validation made relative to western Atlantic NOAA/NDBC buoys that recorded significant wave heights exceeding 6 m (19.7 ft). Bulk validation statistics indicate a high skill of operational wave forecasts up to and beyond the 3-day range. Event-based validation reveals a remarkably high skill of NCEP’s wave ensemble system, with significant added value in its data for longer forecasts beyond the 72-h range. The study concludes with considerations about the extent of severe sea-state footprints during Sandy, the dissemination of real-time wave forecasts, and its impacts to emergency management response, as well as recent upgrades and future developments at NCEP that will improve the skill of its current wave forecasting systems, resulting in more reliable wave forecasts during life-threatening severe storm events in the future.

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Hendrik L. Tolman, Jose-Henrique G. M. Alves, and Yung Y. Chao

Abstract

The accuracy of the operational wave models at the National Centers for Environmental Prediction (NCEP) for sea states generated by Hurricane Isabel is assessed. The western North Atlantic (WNA) and the North Atlantic hurricane (NAH) wave models are validated using analyzed wind fields, and wave observations from the Jason-1 altimeter and from 15 moored buoys. Both models provided excellent guidance for Isabel in the days preceding landfall of the hurricane along the east coast of the United States. However, the NAH model outperforms the WNA model in the initial stages of Isabel, when she was a category 5 hurricane. The NAH model was also more accurate in providing guidance for the swell systems arriving at the U.S. coast well before landfall of Isabel. Although major model deficiencies can be attributed to shortcomings in the driving wind fields, several areas of potential wave model improvement have been identified.

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Yung Y. Chao, Jose-Henrique G. M. Alves, and Hendrik L. Tolman

Abstract

A new wind–wave prediction model, referred to as the North Atlantic hurricane (NAH) wave model, has been developed at the National Centers for Environmental Prediction (NCEP) to produce forecasts of hurricane-generated waves during the Atlantic hurricane season. A detailed description of this model and a comparison of its performance against the operational western North Atlantic (WNA) wave model during Hurricanes Isidore and Lili, in 2002, are presented. The NAH and WNA models are identical in their physics and numerics. The NAH model uses a wind field obtained by blending data from NCEP’s operational Global Forecast System (GFS) with those from a higher-resolution hurricane prediction model, whereas the WNA wave model uses winds provided exclusively by the GFS. Relative biases of the order of 10% in the prediction of maximum wave heights up to 48 h in advance, indicate that the use of higher-resolution winds in the NAH model provides a successful framework for predicting extreme sea states generated by a hurricane. Consequently, the NAH model has been made operational at NCEP for use during the Atlantic hurricane season.

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Ricardo Martins Campos, Jose-Henrique G. M. Alves, Stephen G. Penny, and Vladimir Krasnopolsky

Abstract

The error characteristics of surface waves and winds produced by ensemble forecasts issued by the National Centers for Environmental Prediction are analyzed as a function of forecast range and severity. Eight error metrics are compared, separating the scatter component of the error from the systematic bias. Ensemble forecasts of extreme winds and extreme waves are compared to deterministic forecasts for long lead times, up to 10 days. A total of 29 metocean buoys is used to assess 1 year of forecasts (2016). The Global Wave Ensemble Forecast System (GWES) performs 10-day forecasts four times per day, with a spatial resolution of 0.5° and a temporal resolution of 3 h, using a 20-member ensemble plus a control member (deterministic) forecast. The largest errors in GWES, beyond forecast day 3, are found to be associated with winds above 14 m s−1 and waves above 5 m. Extreme percentiles after the day-8 forecast reach 30% of underestimation for both 10-m-height wind (U10) and significant wave height (Hs). The comparison of probabilistic wave forecasts with deterministic runs shows an impressive improvement of predictability on the scatter component of the errors. The error for surface winds drops from 5 m s−1 in the deterministic runs, associated with extreme events at longer forecast ranges, to values around 3 m s−1 using the ensemble approach. As a result, GWES waves are better predicted, with a reduction in error from 2 m to less than 1.5 m for Hs. Nevertheless, under extreme conditions, critical systematic and scatter errors are identified beyond the day-6 and day-3 forecasts, respectively.

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Jose Henrique G. M. Alves, Michael L. Banner, and Ian R. Young

Abstract

The time-honored topic of fully developed wind seas pioneered by Pierson and Moskowitz is revisited to review the asymptotic evolution limits of integral spectral parameters used by the modeling community in the validation of wind-wave models. Discrepancies are investigated between benchmark asymptotic limits obtained by scaling integral spectral parameters using alternative wind speeds. Using state-of-the-art wind and wave modeling technology, uncertainties in the Pierson–Moskowitz limits due to inhomogeneities in the wind fields and contamination of the original data by crossing seas and swells are also investigated. The resulting reanalyzed database is used to investigate the optimal scaling wind parameter and to refine the levels of the full-development asymptotes of nondimensional integral wave spectral parameters used by the wind-wave modeling community. The results are also discussed in relation to recent advances in quantifying wave-breaking probability of wind seas. The results show that the parameterization of integral spectral parameters and the scaling of nondimensional asymptotes as a function of U 10 yields relations consistent with similarity theory. On the other hand, expressing integral spectral parameters and scaling nondimensional asymptotes as a function of u∗ or alternative proposed scaling wind speeds yields relations that do not conform to similarity requirements as convincingly. The reanalyzed spectra are used to investigate parameter values and shapes of analytical functions representing fully developed spectra. These results support an analytical form with a spectral tail proportional to f −4.

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Cátia C. Azevedo, Carolina M. L. Camargo, José Alves, and Rui M. A. Caldeira

Abstract

The interaction between the incoming winds and high mountainous islands produces a wind-sheltered area on the leeward side, known as the atmospheric wake. In addition to weaker winds, the wake is also characterized by a clearing of clouds, resulting in intense solar radiation reaching the sea surface. As a consequence, a warm oceanic wake forms on the leeward side. This phenomenon, detectable from space, can extend 100 km offshore of Madeira, where the sea surface temperature can be 4°C higher than the surrounding oceanic waters. This study considers in situ, remote sensing, and ocean circulation model data to investigate the effects of the warm wake in the vertical structure of the upper ocean. To characterize the convective layer (25–70 m) developing within the oceanic wake, 200 vertical profiles of temperature, salinity, and turbulence were considered, together with the computation of the density ratio and Turner angle. In comparison with the open-ocean water column, wake waters are strongly stratified with respect to temperature, although highly unstable. The vertical profiles of salinity show distinct water parcels that sink and/or rise as a response to the intense heat fluxes. During the night, the ocean surface cools, leading to the stretching of the mixed layer, which was replicated by the ocean circulation model. In exposed, nonwake regions, however, particularly on the southeast and north coasts of the island, the stretching of the mixed layer is not detectable.

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Ricardo Martins Campos, Vladimir Krasnopolsky, Jose-Henrique G. M. Alves, and Stephen G. Penny

Abstract

Artificial neural networks (ANNs) applied to nonlinear wave ensemble averaging are developed and studied for Gulf of Mexico simulations. It is an approach that expands the conservative arithmetic ensemble mean (EM) from the NCEP Global Wave Ensemble Forecast System (GWES) to a nonlinear mapping that better captures the differences among the ensemble members and reduces the systematic and scatter errors of the forecasts. The ANNs have the 20 members of the GWES as input, and outputs are trained using observations from six buoys. The variables selected for the study are the 10-m wind speed (U10), significant wave height (Hs), and peak period (Tp) for the year of 2016. ANNs were built with one hidden layer using a hyperbolic tangent basis function. Several architectures with 12 different combinations of neurons, eight different filtering windows (time domain), and 100 seeds for the random initialization were studied and constructed for specific forecast days from 0 to 10. The results show that a small number of neurons are sufficient to reduce the bias, while 35–50 neurons produce the greatest reduction in both the scatter and systematic errors. The main advantage of the methodology using ANNs is not on short-range forecasts but at longer forecast ranges beyond 4 days. The nonlinear ensemble averaging using ANNs was able to improve the correlation coefficient on forecast day 10 from 0.39 to 0.61 for U10, from 0.50 to 0.76 for Hs, and from 0.38 to 0.63 for Tp, representing a gain of five forecast days when compared to the EM currently implemented.

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