Development and Implementation of Wind-Generated Ocean Surface Wave Modelsat NCEP*

Tolman, Hendrik L.; Balasubramaniyan, Bhavani; Burroughs, Lawrence D.; Chalikov, Dmitry; Chao, Yung Y.; Chen, Hsuan S.; Gerald, Vera M.

doi:10.1175/1520-0434(2002)017<0311:daiowg>2.0.co;2

Cited by 311 publications

(193 citation statements)

References 16 publications

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“…2) used in the ocean wave model was constructed from the General Bathymetric Chart of the Waves for Geophysical and Other Applications) (Stopa et al, 2016) global wave hindcast, conducted using WAVEWATCH-III wave model (Tolman et al, 2002). The wave hindcast was constructed using the winds from CFSR (Climate Forecast System 15 Reanalysis) dataset (Saha et al, 2014 …”

Section: Wave Modelmentioning

confidence: 99%

The Effects of Sea Spray and Atmosphere–Wave Coupling on Air–Sea Exchange during Tropical Cyclone

Garg¹,

Ng²,

Srikanth³

2017

Preprint

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Abstract. The study investigates the role of air-sea interface using numerical simulations of an Atlantic Hurricane Arthur (2014). More specifically, present study aims to discern the role ocean surface waves and sea spray play in modulating the intensity and structure of a tropical cyclone (TC). To investigate the effects of ocean surface waves and sea spray, numerical simulations were carried out using a coupled atmosphere-wave model where a sea spray microphysical model was incorporated within the coupled model. Furthermore, this study also explores how sea spray generation can be modelled using wave energy 5 dissipation due to whitecaps, where whitecaps are considered as the primary mode of spray droplets generation at hurricane intensity wind speeds. Three different numerical simulations including sea state dependent momentum flux, sea spray mediated heat flux and combination of former two processes with sea spray mediated momentum flux were conducted. The foregoing numerical simulations were evaluated against the National Data Buoy Center (NDBC) buoy and satellite altimeter measurements as well as a control simulation using an uncoupled atmosphere model. The results indicate that the model simulations 10 were able to capture the storm track and intensity, where the surface wave coupling results in a stronger TC. Moreover, it is also noted that when only spray mediated heat fluxes are applied in conjunction with sea state dependent momentum flux, they result in a slightly weaker TC, albeit stronger compared to the control simulation. However, when spray mediated momentum flux is applied together with spray heat fluxes, it results in a comparably stronger TC. The results presented here alludes to the role surface friction plays in intensification of a TC.

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Section: Wave Modelmentioning

confidence: 99%

The Effects of Sea Spray and Atmosphere–Wave Coupling on Air–Sea Exchange during Tropical Cyclone

Garg¹,

Ng²,

Srikanth³

2017

Preprint

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“…Unfortunately, very little wave period and direction data are available in the SH, and consequently we rely on global wave models such as the ERA-40 reanalysis, and the corrected version produced by Sterl and Caires (2005). Another source is the operational global wave model NWW3 (Tolman et al, 2002). Sterl and Caires (2005) in September 1975, which signals a change in regime with higher H S observed after the dip than before.…”

Section: Variability and Trends Of Global Wave Model Estimated Wave Ementioning

confidence: 99%

Variability and trends in the directional wave climate of the Southern Hemisphere

Hemer

Church

Hunter

2009

Intl Journal of Climatology

243

206

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The effect of interannual climate variability and change on the historic, directional wave climate of the Southern Hemisphere is presented. Owing to a lack of in situ wave observations, wave climate in the Southern Hemisphere is determined from satellite altimetry and global ocean wave models. Altimeter data span the period 1985 to present, with the exception of a 2‐year gap in 1989–1991. Interannual variability and trends in the significant wave height are determined from the satellite altimeter record (1991 to present), and the dominant modes of variability are identified using an empirical orthogonal function (EOF) analysis. Significant wave heights in the Southern Ocean are observed to show a strong positive correlation with the Southern Annular Mode (SAM), particularly during Austral autumn and winter months. Correlation between altimeter derived significant wave heights and the Southern Oscillation Index is observed in the Pacific basin, which is consistent with several previous studies. Variability and trends of the directional wave climate are determined using the ERA‐40 Waves Re‐analysis for the period 1980–2001. Significant wave height, mean wave period and mean wave direction data are used to describe the climate of the wave energy flux vector. An EOF analysis of the wave energy flux vector is carried out to determine the dominant modes of variability of the directional seasonal wave energy flux climate. The dominant mode of variability during autumn and winter months is strongly correlated to the SAM. There is an anti‐clockwise rotation of wave direction with the southward intensification of the Southern Ocean storm belt associated with the SAM. Clockwise rotation of flux vectors is observed in the Western Pacific Ocean during El‐Nino events. Directional variability of the wave energy flux in the Western Pacific Ocean has previously been shown to be of importance to sand transport along the south‐eastern Australian margin, and the New Zealand region. The directional variability of the wave energy flux of the Southern Ocean associated with the SAM is expected to be of importance to the wave‐driven currents responsible for the transport of sand along coastal margins in the Southern Hemisphere, in particular those on the Southern and Western coastal margins of the Australian continent. Copyright © 2009 Royal Meteorological Society

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“…While ERA-I includes wave data through a coupled model system, Chawla et al [2013] utilized CFSR surface winds to generate hindcast wave data with WAVEWATCH III of Tolman et al [2002]. showed ERA-I is more consistent through time, while CFSR better resolves the upper percentiles and variability in comparison to buoy and altimetry measurements.…”

Section: Introductionmentioning

confidence: 99%

Periodicity and patterns of ocean wind and wave climate

Stopa

Cheung

2014

JGR Oceans

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The Climate Forecast System Reanalysis (CFSR) provides a wealth of information spanning 1979-2009 for investigation of ocean wind and wave climate. Preprocessing of the data is necessary to remove the dominant seasonal signals and to create time series of semimonthly averaged wind speed and significant wave height over a 0.5 global grid. We perform an empirical orthogonal function (EOF) analysis to extract the dominant space-time patterns. The results for the three major ocean basins show strong zonal structures in the winds and saturation of the swells corroborating prior works with various data sets. We reexamine the CFSR data in the frequency domain to identify periodic signals associated with published climate indices. The Fourier transform of the preprocessed time series generates spectra ranging from 1 month to 15 years period for an EOF analysis. The results demonstrate the spatial structures and periods of climate phenomena. The Arctic Oscillation dominates the Atlantic basin with a broad range of intra-annual signals off the European coasts. The Indian and Pacific Oceans are strongly influenced by inter-annual cycles of the El Niño Southern Oscillation (ENSO) and the Antarctica Oscillation. The Indian Ocean also has strong intra-annual components ranging from 50 to 80 days period. The ENSO proves to be a ubiquitous signal around the globe, and in particular, saturates the Pacific with strong influences in the equatorial region and the Southern Hemisphere Westerlies. A commonality of all basins is that the magnitude and the spatial structure of the intra-annual and inter-annual signals are similar suggesting a wide range of periods in each of the climate cycles examined.

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Development and Implementation of Wind-Generated Ocean Surface Wave Modelsat NCEP*

Cited by 311 publications

References 16 publications

The Effects of Sea Spray and Atmosphere–Wave Coupling on Air–Sea Exchange during Tropical Cyclone

The Effects of Sea Spray and Atmosphere–Wave Coupling on Air–Sea Exchange during Tropical Cyclone

Variability and trends in the directional wave climate of the Southern Hemisphere

Periodicity and patterns of ocean wind and wave climate

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