ASAP OSSE #1 – N Gliders per Track

1. ASAP OSSE #1 – N Gliders per Track OSSE #1 being guided by “ASAP Team Adaptive Sampling Plan for Gliders in 2006 Field Experiment” – 27 July 2005 “A coordinated sampling pattern for the glider fleet that has been optimized with respect to the … objectives is referred to as an Optimal Coordinated Trajectory (OCT). Coordination refers to prescription of the relative location of all gliders (as a function of time). The plan assumes a minimum of 9 or 10 gliders, all to be coordinated as part of every OCT.” Robinson, Haley, Leslie

2. ASAP OSSE #1 – N Gliders per Track OSSE Domains AOSN2 and ASAP Domains ASAP “Race-Tracks” Utilizes HOPS re-analysis with free surface model (no tides) http://oceans.deas.harvard.edu/AOSN2/OSSE2005/Exp0001/

3. ASAP OSSE #1 – N Gliders per Track OSSE Definition • Ability of N gliders to quantitatively represent a simulated “true” ocean with and without melding with dynamics • Without dynamics: objectively analyze • After each complete circuit of N gliders (one circuit OA) • Once per day (more realistic) (one day OA) • With dynamics: assimilate data once per day and compare • A priori estimate • A posteriori estimate • Compare these estimates with once a day OA’s above

4. ASAP OSSE #1 – N Gliders per Track Experiment Definition • Experiment #1 – simple ideal glider model extracting simulated CTD’s at regular intervals (mimics moving at constant speed) • Additional experiments: • Effects of currents • Glider drop-outs • Realistic SPRAY and SLOCUMB glider models • Tides and varying atmospheric forcing • Alternative tracks

5. ASAP OSSE #1 – N Gliders per Track Experiment Details • Ave. spacing by gliders during AOSN-II (SIO: 1.59km, 2.26hr; WHOI: 0.325km, 0.631hr) • HOPS model resolution is ~1.5km, so the SIO gliders were chosen with the profile spacing reduced to the model resolution. • Time for single glider to complete one track is ~3.6 days, but temporal spacing was reduced to ~1.85 hr to reduce total travel time for a single glider to exactly 3 days. • Varying number (1-12 and 39) of gliders were used to sample HOPS re-analysis fields (39 is all grid points along-track)

6. ASAP OSSE #1 – N Gliders per Track • Profiles from one circuit of N simulated gliders are OA’d and that OA field is compared on a point-by-point basis to the re-analysis field at the mid-point of the time window. • Six (6) gliders provides the best result. More than six provides no additional improvement.

7. 1 glider per track Truth 10m Temp. 14 Aug. 3 gliders per track 6 gliders per track

8. 1 glider per track Truth 10m Temp. 28 Aug. 3 gliders per track 6 gliders per track

9. 1 glider per track 2 gliders per track 10m TRMS 3 gliders per track 6 gliders per track

10. One circuit of data Temperature RMS Salinity RMS

11. ASAP OSSE #1 – N Gliders per Track • Profiles from any one-day window from N simulated gliders are OA’d and that OA field is compared on a point-by-point basis to the re-analysis field at the mid-point in time of the one-day window. • The 39 glider comparison has not been done due to computational considerations. • Five (5) gliders provides the best result. More than five provides no additional improvement. However, five provides only marginally better results than does three (3) gliders.

12. 1 glider per track Truth 10m Temp. 14 Aug. 3 gliders per track 5 gliders per track

13. 1 glider per track 2 gliders per track 10m TRMS 3 gliders per track 5 gliders per track

14. One day of data Temperature RMS Salinity RMS

15. OSSE Research via HOPS/ESSE • Data for OSSEs and baselines • Real-data denials (DA of only a subset of AOSN-II data) • Synthetic ocean sub-sampling (Twin experiment using model data) • Baselines: (i) Model with no DA, (ii) Real data set, and (iii) Re-analysis • Select sub-sampling scenarios and compare the results of their DA, e.g. • Sample the boundary versus sample the interior • Sample upstream/downstream • Impact of specific data types (e.g. SST, etc) • Select optimal rectangular track location/properties (adaptive sampling) • Quantitative Objectives, e.g. • Minimize overall normalized uncertainties in ASAP region • Minimize (heat) flux/budget uncertainties • Maximize coverage, uncertainty reduction and dynamical hot-spots • OSSE Sensitivity Studies • Dynamical regimes (2 upwelling and 1 relaxation event) • Acoustics (FAF05: Wang, Schmidt) and LCSs uncertainties (with Lekien) • Predictability limit/Predictive capability

16. Pi u u0(t) x(t) ug Pi-1 Our Gliders Models • 0th Order: CTD model • Take a vertical profile every Dx km and Dt seconds, along a specified track. During AOSN-II: SIO Spray: Dx= 1.59 km, Dt = 2.26 hours • WHOI Slocumb: Dx= 0.325 km, Dt = 0.631 hours • Model:Fixed track, ug=cnst, w=1 and pitch angle a=900 • 1th Order: Adaptive Bearing, Constant Speed model • Glider sails at constant speed (|u0| , |w0| )towards set of waypoints, with a fixed pitch angle a0 in the vertical (turns at 5 m from the surface and from the bottom or max depth, e.g. 400 m) • Is advected by estimates of ocean currents (u,w) and continuously updates its bearing towards next waypoint • Model: • ug = u0(t) + u • w = w0(a(t)) + w witha(t) = §a0 • u0(t)= f(x(t), Pi )