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SIO ‘Spray’ Gliders for ASAP Goals Contribute to synoptic mapping & upwelling heat budget analysis Develop methods for array optimization & automatic assistance of glider control Examine development of surface and bottom boundary layers through the upwelling and relaxation cycle (Thorpe scales and profiles of U, T and S.) Observations 4 gliders capable to 1500 m to maintain an array for 4-6 weeks patrolling at ~ 25 km/day SBE CTD, Sontek ADP, Fluorometer Real time T, S, U at 4-m resolution. Record T, S at ~ 10-cm resolution
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1.The error from objective mapping provides a metric for optimizing sampling arrays. 2.Fratantoni’s rule: A good array should yield data that can be analyzed without model assimilation (WOMA). 3.Direct minimization of mapping error leads to arrays that are not very useful WOMA. 4. A hybrid approach – specify general structure of array to make data useable WOMA and adjust the parameters of these structures to optimize mapping skill. Maintaining Glider Arrays
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Direct Optimization A glider is imagined to produce a sample every dt while traveling at speed U After each dt the glider is allowed to adopt a new heading Each track is given the score equal to the time integral of the mapping error over some specified rectangular region The mapping error is based on a homogeneous stationary signal covariance of the form C = A exp [-(x 1 -x 2 ) 2 /L 2 -(y 1 -y 2 ) 2 /L 2 -(t 1 -t 2 ) 2 /T 2 which it makes it feasible to compute the area-average square error analytically.
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What are the Scales L and T? Even combined WHOI & SIO AOSN-II glider data does not define the full anisotropic and inhomogeneous covariance. Appropriate “mean” temperature is A(t) + B(t) x Doffshore
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Scales of Temperature Evidence for anisotropy and offshore dependence of scales is weak Depth Half variance in noise Cross-Shelf slightly longer than alongshore
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Depth Isotropic Correlation of Temperature Weak dependence on offshore distance 0.5 Correlation L ~ 15 km T ~ 2 days
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Direct Optimization Results Unless constrained by the boundaries of the area of interest or by a nearby sample, “optimal” trajectories tend to be aimless wandering around an area of one correlation length on a side. These array paths are not useful WOMA although they score well in area average mapping error.
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Example of Direct Optimization
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(Naomi’s) Hybrid Approach Design generalized arrays of glider tracks that would allow interpretation WOMA. Then use objective mapping skill to optimize parameters of the generalized array. a b c Modest expansion of the search for optimal a, b and c might provide assistance in dealing with unplanned factors like failures or the need to re-power some gliders
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5 gliders 100 km x 30 km One vehicle per racetrack, all moving in the same directions and at the same position of their own racetrack.
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5 gliders 100 km x 80 km One vehicle per racetrack, all moving in the same directions and at the same position of their own racetrack.
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