McWilliams, Sam

Raghukumar, Kaus

English

ScholarWorks @ The University of New Orleans

University of New Orleans 
ScholarWorks@UNO 
Ocean Waves Workshop 
Oct 17th, 11:15 AM - 11:30 AM 
Ocean Waves Workshop 2019 Session 3 Notes 
Kaus Raghukumar 
Integral Consulting Inc., Santa Cruz, CA 
Sam McWilliams 
Integral Consulting Inc., Santa Cruz, CA 
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Raghukumar, Kaus and McWilliams, Sam, "Ocean Waves Workshop 2019 Session 3 Notes" (2019). Ocean 
Waves Workshop. 2. 
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Session 3 – Wave Modeling Frameworks 
Rapporteurs:   1Kaus Raghukumar, Ph.D., and 1Sam McWilliams, E.I.T. 
1Integral Consulting 
 
Running ocean models and managing the results remains a computationally demanding task.  
Previously, operational wave models for the Navy were being run at NAVO and FNMOC, but 
now all the models are run at NAVO, with a backup run at FNMOC.  
Littoral forecasts are of particular importance, as a result of which there are 300 Simulating 
WAves Nearshore (SWAN) domains nested within WAVEWATCH III (WIII). The new ability 
for WWIII to run with an implicit numerical schemes (similar to SWAN) can help eliminate 
running the SWAN littoral models.  Wind forcing the wave models are currently obtained from 
the Navy Global Environmental Model (NAVGEM), a numerical weather prediction computer 
simulation run by the United States Navy's Numerical Meteorology and Oceanography Center.  
Modelers don’t always understand operational requirements that would guide visualization of 
output.  Efforts to document requirements would support modeling efforts and optimizing the 
siting of sensors. 
Predictions must be accurate and robust or they are not useful. 
 
The scarcity of oceanographic data, especially during extreme events, can lead to uncertainty in 
wave and surge models and predictions that support operators.  In the nearshore, the general lack 
of resolution for bathymetry and bed type can be limiting.  
 
Information on uncertainty may help planners, ship drivers, and systems operators to make better 
decisions, especially when they are comparing multiple types of products.    
 
Data assimilation systems should be used to help reduce and quantify uncertainty. 
The aim of data assimilation is to improve forecasts. 
Most data assimilation methods used in wave forecasting are either sequential or dynamic. 
Sequential methods are time independent while dynamical ones are time dependent.  
Operational assimilation of wave data for wave prediction should focus on dynamic data 
assimilation methods.  The Naval Research Laboratory has developed a 4D variational data 
assimilation system (4DVAR) for Wave Spectra. Currently, optimal interpolation schemes are 
being implemented via the Navy Coupled Ocean Data Assimilation. (NCODA) architecture. The 
entire wave spectrum is uniformly adjusted based on altimeter measurements of Hs.  
3D and 4DVAR schemes are currently under development but it will be a while before they are 
transitioned to operational forecasting.  
Cloud computing presents opportunities related to shared data processing workflows that utilize 
common, adaptable software to handle data ingest and storage 
Cloud platforms offer new ways for scientists to observe and predict waves.  
Operation of models aboard vessels, especially to support operators in communication limited 
and denied environments.  
Verification metrics should reflect the operator’s requirements for information and be collected 
and analyzed by third parties, not the model developer.   
Seminal datasets consisting of extreme wave measurements should be included as a component 
of any modeling test beds.  Seminal data sets are essential to support inter-model comparisons 
and performance verification.  
 


Ocean Waves Workshop 2019 Session 3 Notes

https://scholarworks.uno.edu/cgi/viewcontent.cgi?article=1086&amp;context=oceanwaves

Ocean Waves Workshop 2019 Session 3 Notes

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