asap osse #1 – n gliders per track

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

http://oceans.deas.harvard.edu/AOSN2/OSSE2005/Exp0001/

AOSN2 and ASAP Domains ASAP “Race-Tracks”

Utilizes HOPS re-analysis with free surface model (no tides)


OSSE Domains

• Ability of N gliders to quantitatively represent a simulated “true” ocean with and without melding with dynamics

• Without dynamics: objectively analyzei. After each complete circuit of N gliders (one circuit OA)

ii. Once per day (more realistic) (one day OA)

• With dynamics: assimilate data once per day and comparei. A priori estimateii. A posteriori estimate

Compare these estimates with once a day OA’s above


OSSE Definition

• Experiment #1 – simple ideal glider model extracting simulated CTD’s at regular intervals (mimics moving at constant speed)

• Additional experiments:

i. Effects of currents

ii. Glider drop-outs

iii. Realistic SPRAY and SLOCUMB glider models

iv. Tides and varying atmospheric forcing

v. Alternative tracks


Experiment Definition

• 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)


Experiment Details

• 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.


10m Temp.14 Aug.

Truth 1 gliderper track

3 glidersper track

6 glidersper track

10m Temp.28 Aug.


3 glidersper track

6 glidersper track

10m TRMS

2 glidersper track

3 glidersper track

6 glidersper track

1 gliderper track

Temperature RMS Salinity RMS

One circuit of data

• 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.


10m Temp.14 Aug.


3 glidersper track

5 glidersper track

10m TRMS

2 glidersper track

3 glidersper track

5 glidersper track

1 gliderper track

Temperature RMS Salinity RMS

One day of data

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

Our Gliders Models• 0th Order: CTD model

- Take a vertical profile every x km and t seconds, along a specified track. During AOSN-II: SIO Spray: x = 1.59 km, t = 2.26 hours

WHOI Slocumb: x = 0.325 km, t = 0.631 hours

- Model: Fixed track, ug=cnst, w=1 and pitch angle =900

• 1th Order: Adaptive Bearing, Constant Speed model

- Glider sails at constant speed (|u0| , |w0| ) towards set of waypoints, with a fixed pitch angle 0 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((t)) + w with t= § 0

- u0(t) = f(x(t), Pi )

uu0(t)

ug

Pi

Pi-1

x(t)

asap osse #1 – n gliders per track

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