Motivation Method Ground Truth tests Electro-optical Estimation
of Wave Dissipation Rob Holman, Oregon State University
Slide 2
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.
Slide 3
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).
Slide 4
Breaker Detection 1.Based on cross-shore time stacks
Slide 5
Breaker Detection 1.Based on cross-shore time stacks
2.De-propagate waves using celerity from cBathy
Slide 6
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
Slide 7
Oregon State University Comparison to Ground Truth Do manual
breaker counts at five positions y = 690, 09/09/10, Duck
Slide 8
Oregon State University Comparison of Manual and Automated
Breaker Counts Manual Difference Automatic T p = 12.5s H s =
0.87m
Slide 9
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
Slide 10
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
Slide 11
Oregon State University Comparison of Q b With Timex
Slide 12
Oregon State University Comparison of Q b With Timex
Slide 13
Oregon State University Comparison of Q b With SWAN
Dissipation
Slide 14
Comparison With SWIFT Dissipation Data Create EO time series
from cBathy time series collections for SWIFT trajectory
(x,y,t)
Slide 15
Comparison With SWIFT Dissipation Data SWIFT dissipation Argus
intensity
Slide 16
Comparison With SWIFT Dissipation Data SWIFT breaker detects
Argus intensity Good agreement with SWIFT manual counts Working on
comparison with actual dissipation time series.
Slide 17
Fusion with Radar, DA with Models Radar Time Exposure (Diaz and
Haller) ROMS Dissipation (Moghimi and Ozkan-Haller)
Slide 18
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