SW06/NLIWI Breakout Sessions Physical Oceanography January 31, 2007

Tidal heights - pressure sensors on many devices in array mooring guys could construct surface height field across array - what precision required? (Wilkin volunteered to assimilate this data into model) surface waves – Graber surface roughness variability on NLIW scales? fcn(winds, NLIW speed) - (Plant/Lyzenga) front / mesoscale / eddies effects on c(x,y,z,t) Wilkin / Glenn / Gawark. NLIW effects on c(x,y,z,t) Incorporation of multiple analyses with above task + wavefront analysis - not sure who leads this yet? Acoustics requests from yesterday

A. Wave Front Analysis Wave front analysis / arrival times / all platforms (moored/ship/aircraft/gliders) 2D mapping as a function of time  Primary objective: to determine mechanisms of ISW generation Specific goals: 1. Identify generation sites 2. Identify generation times 3. assess geometrical attenuation Approach: 1. Perform analysis for one wave (named waves Rosey(Aug 17,18) /Sonny(Aug19) (Mohsen / Kevin / Aug 9)) 2. Extend to AWACS period (mesoscale) 3. Extend to all waves mooring lines PIs: Nash, Duda, Glenn, Plant, Henyey, Moum, Graber

B. c(x,y,z,t) wave fronts (x,y,t) + vertical profile data + currents + modeling: Part I: large scale (Wilkin- all profile data) Part II: NLIW scale (include part A) (PIs: Duda, Nash ) - 1 st do test wave - then all waves Merged in array mooring lines

C. Decomposition of current field Decomposition of current field: Near-f Tidal (includes M2 internal tides) Mesoscale Q: How do these modulate phasing of tides and NLIW? Q: How do these affect arrival times at a point? Q: How are these interpreted in terms of generation mechanisms? DATA: Moored data Codar (array coverage 50% of time – high surface wave periods) Model data Ship data – vertical profiles (marginal) Number of sites with ADCPs? (~12) PIs: Glen, Gawark, Wilkin

C. (cont’d) / Why did largest NLIWs occur in NEAP (not spring) tides? (Both larger and more frequent) Q: Role of mesoscale in modulating NLIW climate? DATA: Mooring SAR ScanFish Glider Obs. Approach: (identify key frontal sharpness features) 1.Investigate changes in stratification due to varying runoff and heating over the experimental time period 2.Use of 2005 data: Thermistor chains and gliders 2003, 2004 glider data? Modeling Approach: Testing Variations in stratifications (N 2 ) against bathymetry, currents with small scale models to see what conditions are required to sharpen the front enough to generate waves How is amplitude of internal tide affected? PIs: Scott Glenn, Gallacher, Glenn Gawarkiewicz, Wilkin PIs: Alberto Scotti, Gallacher

D. FUNDAMENTAL PROBLEM Energy Evolution (losses from barotropic tides- global consequence) Long range propagation: Turbulence Upper shear layer and bottom layer Geometrical spreading (wave front analysis) Wave-wave interactions Group/group interactions Energy interactions Rotation effects - interactions with pre-existing near-f wave field Transition from depression to elevation waves Generation: Energy sources Steepening of stratification into borelike form Bore/wave transitions Solibore propagation vs generation of waves from bore Modeling (Scotti) PIs: Henyey, Moum, Scotti, Plant PIs: Nash, Henyey, Moum, Scotti

Remote Sensing of NLIW E. Magnetic anomalies associated to NLIW NLIW current fields Walter Podney measured magnetic signatures of waves (Science years ago), not from an airplane – tower measurements - used gradiometer Need data from flights PIs: Averra / Gallacher (Moum will provide ADCP data from overflight times) F. HF Acoustics: remote sensing of + turbulence waves step 1: Sources of scatteringPIs: Lavery/Chu step 2: Quantification of turbulence PIs: Lavery/Chu/Moum

G. Remote Sensing: Using and Understanding IW Surface Microwave Signatures Plant, Lyzenga, Wackerman, Graber, Williams Correlations of shipboard and radar ADCP (Endeavor/Oceanus) ASIS near-surface ADV / radar passes

J. Group/Group Interactions DATA (sketchy info): SAR/Endeavor/Oceanus/Moorings SAR provides long range coverage but does not show evolution Ships provide detailed but spatially limited anecdotes Who wins? Who Loses? Is this an energetics issue? What factors determine this? What happens at the cusps? Are lines formed from mutliple point sources? cusps PIs: Graber, Henyey, Moum, Plant, Duda, Nash, Gallacher Daryl Holm / (Holm postdoc)

H. Physical structure of waves Define from observations and small scale models. How is this represented by models? Local wave-following LES models, DNS? Small-scale 3-dimensionality of wave fronts (ship board observations) PIs: Scotti, Moum, Henyey, Nash, Graber, Lavery, Chu How to parameterize this ? Models resolve this Q? How can models get turbulent losses / shelf mixing correctly?

K. Variations in waveforms Under what conditions is each wave form generated? Objective - predictability - effects on acoustics Depression waves at shelf break Elevation waves at shelf break Change in sign of depression to elevation waves Varicose waves DATA: moored ship Separate issue? - Variations in number of waves and packets PIs – Moum / Henyey / Nash / Scotti / Lyzenga / Plant

Individual PIs Submit some form of documentation of data to Art Newhall?

Remote Sensing: Using and Understanding IW Surface Microwave Signatures Bill Plant, David Lyzenga, Chris Wackerman, Niel Williams, Hans Graber, Ellen Lettvin

Using Microwave Images: Kinematics Locate IWs – Lettvin, Graber with satellite SAR Follow IWs as they propagate – Plant from airplane and ship View crossing or merging IWs – Plant from airplane and ship Study crest lengths and curvature – Lettvin, Graber with satellite SAR; Plant from aircraft data

Understanding Microwave Imagery Correlate microwave imagery with surface currents – Lyzenga with ships’ radars; Plant with aircraft and ship data from his radars Correlate satellite data with ship, aircraft data – Lettvin with input from Plant, Lyzenga Apply action conservation principles to understand microwave signatures – Lyzenga, Wackerman, Lettvin, Plant Compare predictions with satellite, aircraft, and ship microwave data – Lyzenga, Wackerman, Lettvin, Plant Supply wave spectra across IWs for comparison with predictions – Lyzenga from video; Williams from ASIS buoys, WAMOS data

Using Microwave Imagery: Dynamics Infer subsurface structure from surface microwave signatures of cross section and velocity – All remote sensing PIs: This is the holy grail!