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Motivation Method Ground Truth tests Electro-optical Estimation of Wave Dissipation Rob Holman, Oregon State University

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Why We Care About Dissipation Dissipation is the dominant remote sensing signal Dissipation is key to energy and momentum fluxes Dissipation would be a powerful DA variable. DARLA is exploring both Q b and roller length methods.

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Why We Care About Dissipation Dissipation is key to radiation stress gradients that drive circulation within the surf zone: f b can be estimated from breaker detections, while h is estimated from cBathy All can be derived from all remote sensing modalities Janssen and Battjes, 2007: where f b is the frequency of breaking (=N b /tau).

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Breaker Detection 1.Based on cross-shore time stacks

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Breaker Detection 1.Based on cross-shore time stacks 2.De-propagate waves using celerity from cBathy

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Breaker Detection 1.Based on cross-shore time stacks 2.De-propagate waves using celerity from cBathy 3.Detection of rising edges using Sobel filter

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Oregon State University Comparison to Ground Truth Do manual breaker counts at five positions y = 690, 09/09/10, Duck

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Oregon State University Comparison of Manual and Automated Breaker Counts Manual Difference Automatic T p = 12.5s H s = 0.87m

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Oregon State University Comparison of Manual and Automated Breaker Counts 95% sure #breakers correct to within 5% for y > 300m (10% for y > 500m for strong storm with mostly 100% breaking) T p = 5.6 s H s = 1.06 m

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Oregon State University Products: Breaker PDFs Individual wave detection x - 320 mx - 244 m x - 112 m x - 156 m Red = N b Blue = N Tot Green = Rayleigh Theory

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Oregon State University Comparison of Q b With Timex

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Oregon State University Comparison of Q b With Timex

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Oregon State University Comparison of Q b With SWAN Dissipation

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Comparison With SWIFT Dissipation Data Create EO time series from cBathy time series collections for SWIFT trajectory (x,y,t)

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Comparison With SWIFT Dissipation Data SWIFT dissipation Argus intensity

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Comparison With SWIFT Dissipation Data SWIFT breaker detects Argus intensity Good agreement with SWIFT manual counts Working on comparison with actual dissipation time series.

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Fusion with Radar, DA with Models Radar Time Exposure (Diaz and Haller) ROMS Dissipation (Moghimi and Ozkan-Haller)

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Summary Dissipation is a dominant remote sensing signal and a key dynamical variable All the component variables can be estimated remotely, hence so can dissipation Struggling with finding ground truth