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condition in both the atmospheric and the ocean models, the net momentum flux is not necessarily conserved. The amount of momentum lost from the atmosphere may be different from the amount of momentum gained by the ocean. A second point is that the surface stress is also, to a large extent, dependent on the sea state, that is, how the wave energy is distributed over the frequency range. A young wind sea is more dominated by waves in the high-frequency part of the spectrum than an old wind sea, whereas
condition in both the atmospheric and the ocean models, the net momentum flux is not necessarily conserved. The amount of momentum lost from the atmosphere may be different from the amount of momentum gained by the ocean. A second point is that the surface stress is also, to a large extent, dependent on the sea state, that is, how the wave energy is distributed over the frequency range. A young wind sea is more dominated by waves in the high-frequency part of the spectrum than an old wind sea, whereas
1. Introduction Direct measurements of sea state are mostly performed by wave buoys or from sensors attached to fixed structures such as drilling platforms. While buoys provide accurate observations, the high costs for initial installation, and ongoing maintenance and communications have tended to limit their deployment to coastal waters, augmented by a sparse deep-water network ( Fig. 1 ). Global coverage of sea state measurements can be provided by satellite observations (e.g., Gonzalez et
1. Introduction Direct measurements of sea state are mostly performed by wave buoys or from sensors attached to fixed structures such as drilling platforms. While buoys provide accurate observations, the high costs for initial installation, and ongoing maintenance and communications have tended to limit their deployment to coastal waters, augmented by a sparse deep-water network ( Fig. 1 ). Global coverage of sea state measurements can be provided by satellite observations (e.g., Gonzalez et
1. Introduction Current hurricane models are unable to produce high wind speeds when using traditional air–sea flux parameterizations. Emanuel (1995) showed that the ratio of the enthalpy exchange coefficient to the drag coefficient controls the maximum wind speed and that traditional parameterizations produce too low of a ratio when extrapolated to high wind speeds. Consequently, some combination of a higher enthalpy exchange coefficient and a lower drag coefficient is necessary to simulate
1. Introduction Current hurricane models are unable to produce high wind speeds when using traditional air–sea flux parameterizations. Emanuel (1995) showed that the ratio of the enthalpy exchange coefficient to the drag coefficient controls the maximum wind speed and that traditional parameterizations produce too low of a ratio when extrapolated to high wind speeds. Consequently, some combination of a higher enthalpy exchange coefficient and a lower drag coefficient is necessary to simulate
. Toba et al. (1990) show that Z ch increases with the wave age if laboratory observations (with very small wave ages) are included. Some studies ( Taylor and Yelland 2001 ; Edson et al. 2013 ) suggest that Z ch increases with wave steepness ( H s / L p , where H s is the significant wave height and L p is the wavelength at the wave spectral peak) in low to medium wind speeds. Under TC conditions the sea state dependence of C d has been addressed in very few observational studies
. Toba et al. (1990) show that Z ch increases with the wave age if laboratory observations (with very small wave ages) are included. Some studies ( Taylor and Yelland 2001 ; Edson et al. 2013 ) suggest that Z ch increases with wave steepness ( H s / L p , where H s is the significant wave height and L p is the wavelength at the wave spectral peak) in low to medium wind speeds. Under TC conditions the sea state dependence of C d has been addressed in very few observational studies
order to have confidence in the extremes of the distributions, many hours of a particular sea state must be modeled. Many of these limitations can be overcome by using the SRS method. First, by incorporating a suitable wave model into the method, the full nonlinearity can be considered. This task is addressed in the present paper. Second, the sea state can be described by a realistic broadbanded and directionally spread spectrum; the former is addressed herein, and the latter is left for a
order to have confidence in the extremes of the distributions, many hours of a particular sea state must be modeled. Many of these limitations can be overcome by using the SRS method. First, by incorporating a suitable wave model into the method, the full nonlinearity can be considered. This task is addressed in the present paper. Second, the sea state can be described by a realistic broadbanded and directionally spread spectrum; the former is addressed herein, and the latter is left for a
illustrate the temporal variation. A star indicates the starting condition, a triangle represents the condition at the maximal wind speed that separates the growing and decaying phases of the event, and a circle denotes the end of the event. Because of the mixed-sea condition, the starting state (indicated by the star) may deviate considerably from the more ideal fetch-limited growth curves. As the wind event begins, the wind sea quickly adjusts to the condition similar to that of the ideal fetch
illustrate the temporal variation. A star indicates the starting condition, a triangle represents the condition at the maximal wind speed that separates the growing and decaying phases of the event, and a circle denotes the end of the event. Because of the mixed-sea condition, the starting state (indicated by the star) may deviate considerably from the more ideal fetch-limited growth curves. As the wind event begins, the wind sea quickly adjusts to the condition similar to that of the ideal fetch
Error Characterization of Significant Wave Heights in Multidecadal Satellite Altimeter Product, Model Hindcast, and In Situ Measurements Using the Triple Collocation Technique1. Introduction Historical wave data are important for many scientific and engineering applications (climate science, marine safety, coastal and offshore structures, coastal risk management). The sea state is observed by a variety of instrument networks, including Voluntary Observing Ships, seismometers, moored and drifting buoys, satellite altimeters, and satellite-borne synthetic aperture radars ( Ardhuin et al. 2019 ). In addition, wave model hindcasts and reanalysis provide accurate
1. Introduction Historical wave data are important for many scientific and engineering applications (climate science, marine safety, coastal and offshore structures, coastal risk management). The sea state is observed by a variety of instrument networks, including Voluntary Observing Ships, seismometers, moored and drifting buoys, satellite altimeters, and satellite-borne synthetic aperture radars ( Ardhuin et al. 2019 ). In addition, wave model hindcasts and reanalysis provide accurate
the Horn of Africa was conducted in sea states of 4 and below (94% of paired observations) ( Fig. 7 ), with most of these attacks launched in relatively calm oceans of sea state 3 and below (wave heights < 1.25 m). Attack success steadily declined as wave heights increased, with threshold behavior less conspicuous than that exhibited by the wind speed results ( Fig. 7 ). Fig . 5. Location of pirate attacks (open circles) and matching satellite observations ( n = 155) used in this study. Black
the Horn of Africa was conducted in sea states of 4 and below (94% of paired observations) ( Fig. 7 ), with most of these attacks launched in relatively calm oceans of sea state 3 and below (wave heights < 1.25 m). Attack success steadily declined as wave heights increased, with threshold behavior less conspicuous than that exhibited by the wind speed results ( Fig. 7 ). Fig . 5. Location of pirate attacks (open circles) and matching satellite observations ( n = 155) used in this study. Black
footing because of the advancing wind field and differences in the base sea state from upstream feeding. To account for the observed systematic deviation from the reference growth curves of the hurricane wind waves in different sectors ( Fig. 2 ), the fetches or durations for H s and T p are allowed to be different. The results show that the effective fetch and duration increase about linearly with the distance r to the measurement location from the hurricane center. The slope s and intercept
footing because of the advancing wind field and differences in the base sea state from upstream feeding. To account for the observed systematic deviation from the reference growth curves of the hurricane wind waves in different sectors ( Fig. 2 ), the fetches or durations for H s and T p are allowed to be different. The results show that the effective fetch and duration increase about linearly with the distance r to the measurement location from the hurricane center. The slope s and intercept
Bimodality of Directional Distributions in Ocean Wave Spectra: A Comparison of Data-Adaptive Estimation Techniquesdistribution of a typical unimodal sea state based on various analysis methods. For the directional spectra, shown in the upper row, there is only a minor difference among the methods, all showing clear unimodality, which is also reflected in the directional distributions shown in the lower row. Multimodality is a persistent feature in the Ekofisk data, where a large fraction of the directional spectra have evidence of bimodality for distributions above the spectral peak. Figure 10 shows occurrences of
distribution of a typical unimodal sea state based on various analysis methods. For the directional spectra, shown in the upper row, there is only a minor difference among the methods, all showing clear unimodality, which is also reflected in the directional distributions shown in the lower row. Multimodality is a persistent feature in the Ekofisk data, where a large fraction of the directional spectra have evidence of bimodality for distributions above the spectral peak. Figure 10 shows occurrences of
