Assimilating global wave model predictions and deep water wave observations in nearshore swell predictions

Assimilating global wave model predictions and deep water wave observations in nearshore swell... AbstractNearshore wave predictions with high resolution in space and time are needed for boating safety, assessment of flood risk, and to support nearshore processes research. Here, we present methods to improve regional, nearshore predictions of swell-band wave energy (0.04 - 0.09Hz) by assimilating local buoy observations into a linear wave propagation model with a priori guidance from global Wave Watch III (WW3) model predictions. Linear wave propagation, including depth-induced refraction and shoaling, and travel time lags, is modeled with self-adjoint backwards ray tracing techniques. The Bayesian assimilation yields smooth, high-resolution offshore wave directional spectra that are consistent with WW3, and with offshore and local buoy observations. Case studies in the Southern California Bight (SCB) confirm that the nearshore predictions at independent (non-assimilated) buoy sites are improved by assimilation compared with predictions driven with WW3 or with a single offshore buoy. These assimilation techniques, valid in regions and frequency bands where wave energy propagation is mostly linear, use significantly less computational resources than nonlinear models and variational methods, and could be a useful component of a larger regional assimilation program. Where buoy locations have historically been selected to meet local needs, these methods can aid in design of regional buoy arrays by quantifying the regional skill improvement for a given buoy observation, and identifying both high-value and redundant observations. Assimilation techniques also identify likely forward model error in the Santa Barbara Channel, where permanent observations or model corrections are needed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Atmospheric and Oceanic Technology American Meteorological Society

Assimilating global wave model predictions and deep water wave observations in nearshore swell predictions

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0426
D.O.I.
10.1175/JTECH-D-17-0003.1
Publisher site
See Article on Publisher Site

Abstract

AbstractNearshore wave predictions with high resolution in space and time are needed for boating safety, assessment of flood risk, and to support nearshore processes research. Here, we present methods to improve regional, nearshore predictions of swell-band wave energy (0.04 - 0.09Hz) by assimilating local buoy observations into a linear wave propagation model with a priori guidance from global Wave Watch III (WW3) model predictions. Linear wave propagation, including depth-induced refraction and shoaling, and travel time lags, is modeled with self-adjoint backwards ray tracing techniques. The Bayesian assimilation yields smooth, high-resolution offshore wave directional spectra that are consistent with WW3, and with offshore and local buoy observations. Case studies in the Southern California Bight (SCB) confirm that the nearshore predictions at independent (non-assimilated) buoy sites are improved by assimilation compared with predictions driven with WW3 or with a single offshore buoy. These assimilation techniques, valid in regions and frequency bands where wave energy propagation is mostly linear, use significantly less computational resources than nonlinear models and variational methods, and could be a useful component of a larger regional assimilation program. Where buoy locations have historically been selected to meet local needs, these methods can aid in design of regional buoy arrays by quantifying the regional skill improvement for a given buoy observation, and identifying both high-value and redundant observations. Assimilation techniques also identify likely forward model error in the Santa Barbara Channel, where permanent observations or model corrections are needed.

Journal

Journal of Atmospheric and Oceanic TechnologyAmerican Meteorological Society

Published: Jul 11, 2017

References

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