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- Author or Editor: Jeffrey L. Hanson x
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Abstract
Wind sea growth and dissipation in a swell-dominated, open ocean environment is investigated to explore the use of wave parameters in air–sea process modeling. Wind, wave, and whitecap observations are used from the Gulf of Alaska surface scatter and air–sea interaction experiment (Critical Sea Test-7, Phase 2), conducted 24 February through 1 March 1992. Wind sea components are extracted from buoy directional wave spectra using an inverted catchment area approach for peak isolation with both wave age criteria and an equilibrium range threshold used to classify the wind sea spectral domain. Dimensionless wind sea energy is found to scale with inverse wave age independently of swell. However, wind trend causes significant variations, such as underdeveloped seas during rising winds. These important effects are neglected in wind-forced air–sea process models.
The total rate of wave energy dissipation is conveniently estimated using concepts from the Phillips equilibrium range theory. Replacing wind speed with wave dissipation rate in the standard power-law description of oceanic whitecap fraction decreases the range of data scatter by two to three orders of magnitude. The improved modeling of whitecaps demonstrates that wave spectral parameters can be used to enhance air–sea process models.
Abstract
Wind sea growth and dissipation in a swell-dominated, open ocean environment is investigated to explore the use of wave parameters in air–sea process modeling. Wind, wave, and whitecap observations are used from the Gulf of Alaska surface scatter and air–sea interaction experiment (Critical Sea Test-7, Phase 2), conducted 24 February through 1 March 1992. Wind sea components are extracted from buoy directional wave spectra using an inverted catchment area approach for peak isolation with both wave age criteria and an equilibrium range threshold used to classify the wind sea spectral domain. Dimensionless wind sea energy is found to scale with inverse wave age independently of swell. However, wind trend causes significant variations, such as underdeveloped seas during rising winds. These important effects are neglected in wind-forced air–sea process models.
The total rate of wave energy dissipation is conveniently estimated using concepts from the Phillips equilibrium range theory. Replacing wind speed with wave dissipation rate in the standard power-law description of oceanic whitecap fraction decreases the range of data scatter by two to three orders of magnitude. The improved modeling of whitecaps demonstrates that wave spectral parameters can be used to enhance air–sea process models.
Abstract
To facilitate investigations of surface wave processes in the open ocean, a wave spectral partitioning method with automated swell tracking and storm source identification capabilities has been developed. These tools collectively form the Wave Identification and Tracking System (WITS) and have been assembled entirely within the Matlab programming environment. A series of directional wave spectra, with supporting wind observations, is the only required input. Wave spectrum peaks representing specific wind sea and swell wave systems are extracted based on topographic minima, with wind sea peaks identified by wave age criteria. A swell tracking algorithm, combined with linear wave theory, provides a unique approach to storm source identification using the assimilated wave system statistics. The nature of the partitioned spectra allows the continuous, automated identification and tracking of multiple swell generation areas over space and time. Over a 6-day wave record in the Gulf of Alaska, 44 specific swell systems are identified, with up to three systems coexisting at any given time. The presence of atmospheric disturbances on surface weather charts validated the storm source predictions for more than 85% of these systems. The results are synthesized to depict the wave evolution history over the duration of the observations.
Abstract
To facilitate investigations of surface wave processes in the open ocean, a wave spectral partitioning method with automated swell tracking and storm source identification capabilities has been developed. These tools collectively form the Wave Identification and Tracking System (WITS) and have been assembled entirely within the Matlab programming environment. A series of directional wave spectra, with supporting wind observations, is the only required input. Wave spectrum peaks representing specific wind sea and swell wave systems are extracted based on topographic minima, with wind sea peaks identified by wave age criteria. A swell tracking algorithm, combined with linear wave theory, provides a unique approach to storm source identification using the assimilated wave system statistics. The nature of the partitioned spectra allows the continuous, automated identification and tracking of multiple swell generation areas over space and time. Over a 6-day wave record in the Gulf of Alaska, 44 specific swell systems are identified, with up to three systems coexisting at any given time. The presence of atmospheric disturbances on surface weather charts validated the storm source predictions for more than 85% of these systems. The results are synthesized to depict the wave evolution history over the duration of the observations.
Abstract
Although mean or integral properties of wave spectra are typically used to evaluate numerical wave model performance, one must look into the spectral details to identify sources of model deficiencies. This creates a significant problem, as basin-scale wave models can generate millions of independent spectral values. To facilitate selection of a wave modeling technology for producing a multidecade Pacific hindcast, a new approach was developed to reduce the spectral content contained in detailed wave hindcasts to a convenient set of performance indicators. The method employs efficient image processing tools to extract windsea and swell wave components from monthly series of nondirectional and directional wave spectra. Using buoy observations as ground truth, both temporal correlation (TC) and quantile–quantile (QQ) statistical analyses are used to quantify hindcast skill in reproducing measured wave component height, period, and direction attributes. An integrated performance analysis synthesizes the TC and QQ results into a robust assessment of prediction skill and yields distinctive diagnostics on model inputs and source term behavior. The method is applied to a set of Pacific basin hindcasts computed using the WAM, WAVEWATCH III, and WAVAD numerical wave models. The results provide a unique assessment of model performance and have guided the selection of WAVEWATCH III for use in Pacific hindcast production runs for the U.S. Army Corps of Engineers Wave Information Studies Program.
Abstract
Although mean or integral properties of wave spectra are typically used to evaluate numerical wave model performance, one must look into the spectral details to identify sources of model deficiencies. This creates a significant problem, as basin-scale wave models can generate millions of independent spectral values. To facilitate selection of a wave modeling technology for producing a multidecade Pacific hindcast, a new approach was developed to reduce the spectral content contained in detailed wave hindcasts to a convenient set of performance indicators. The method employs efficient image processing tools to extract windsea and swell wave components from monthly series of nondirectional and directional wave spectra. Using buoy observations as ground truth, both temporal correlation (TC) and quantile–quantile (QQ) statistical analyses are used to quantify hindcast skill in reproducing measured wave component height, period, and direction attributes. An integrated performance analysis synthesizes the TC and QQ results into a robust assessment of prediction skill and yields distinctive diagnostics on model inputs and source term behavior. The method is applied to a set of Pacific basin hindcasts computed using the WAM, WAVEWATCH III, and WAVAD numerical wave models. The results provide a unique assessment of model performance and have guided the selection of WAVEWATCH III for use in Pacific hindcast production runs for the U.S. Army Corps of Engineers Wave Information Studies Program.
Abstract
A new operational wave forecasting system has been implemented at the National Centers for Environmental Prediction (NCEP) using the third public release of WAVEWATCH III. The new system uses a mosaic of grids with two-way nesting in a single model. This global system replaces a previous operational wave modeling suite (based on the second release of WAVEWATCH III). The new forecast system consists of nine grids at different resolutions to provide the National Weather Service (NWS) and NCEP centers with model guidance of suitable resolution for all areas where they have the responsibility of providing gridded forecast products. New features introduced in WAVEWATCH III, such as two-way nesting between grids and carving out selected areas of the computational domain, have allowed the operational model to increase spatial resolution and extend the global domain closer to the North Pole, while at the same time optimizing the computational cost. A spectral partitioning algorithm has been implemented to separate individual sea states from the overall spectrum, thus providing additional products for multiple sea states. Field output data are now packed in version 2 of the gridded binary (GRIB2) format and apart from the standard mean wave parameters, they also include parameters of partitioned wave spectra. The partitioning is currently limited to three fields: the wind-wave component, and primary and secondary swells. The modeling system has been validated against data using a multiyear hindcast database as well as archived forecasts. A new software tool developed by the U.S. Army Corps of Engineers (USACE) is used to extend the analysis from overall error estimates to separate skill scores for wind seas and swells.
Abstract
A new operational wave forecasting system has been implemented at the National Centers for Environmental Prediction (NCEP) using the third public release of WAVEWATCH III. The new system uses a mosaic of grids with two-way nesting in a single model. This global system replaces a previous operational wave modeling suite (based on the second release of WAVEWATCH III). The new forecast system consists of nine grids at different resolutions to provide the National Weather Service (NWS) and NCEP centers with model guidance of suitable resolution for all areas where they have the responsibility of providing gridded forecast products. New features introduced in WAVEWATCH III, such as two-way nesting between grids and carving out selected areas of the computational domain, have allowed the operational model to increase spatial resolution and extend the global domain closer to the North Pole, while at the same time optimizing the computational cost. A spectral partitioning algorithm has been implemented to separate individual sea states from the overall spectrum, thus providing additional products for multiple sea states. Field output data are now packed in version 2 of the gridded binary (GRIB2) format and apart from the standard mean wave parameters, they also include parameters of partitioned wave spectra. The partitioning is currently limited to three fields: the wind-wave component, and primary and secondary swells. The modeling system has been validated against data using a multiyear hindcast database as well as archived forecasts. A new software tool developed by the U.S. Army Corps of Engineers (USACE) is used to extend the analysis from overall error estimates to separate skill scores for wind seas and swells.
