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  • Author or Editor: Jeffrey L. Hanson x
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Jeffrey L. Hanson
and
Owen M. Phillips

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.

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Jeffrey L. Hanson
,
Barbara A. Tracy
,
Hendrik L. Tolman
, and
R. Douglas Scott

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.

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