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Mapping of the ocean surface wind by ocean acoustic interferometers
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View: Figures


Image of FIG. 1.
FIG. 1.

(Color online) Tracks of named storms in the Atlantic Ocean for 2005. Figure is provided courtesy of the National Oceanic and Atmospheric Administration, National Hurricane Center.

Image of FIG. 2.
FIG. 2.

Sound speed profile characteristic for North Atlantic (taken from Wilson and Makris, (Ref. 7) 2006) used in simulations.

Image of FIG. 3.
FIG. 3.

Propagation factors for different acoustic modes as functions of frequency. The propagation factor is a product of an excitation coefficient of a given mode at the source and a factor exp[−Im (ξ n ) R] representing exponential decay of the mode with distance due to bottom interaction. The excitation coefficients also increase with frequency; however, after a mode reaches the bottom, the attenuation factor dominates (the distance R at the plot was chosen, R = 425 km).

Image of FIG. 4.
FIG. 4.

Eigenvalues of matrix W [see Eq. (14)], which must be inverted for retrieval of the wind field. They exponentially decay with index; however, the leading six eigenvalues are large enough to ensure sufficiently accurate wind retrieval.

Image of FIG. 5.
FIG. 5.

Spatial structure of the wind field corresponding to eigenfunctions of matrix W [see Eq. (14)] due to all 12 eigenfunctions. Only the six first eigenfunctions were used for inversions; the wind field configurations corresponding to the remaining eigenfunctions are unresolved by the current set-up of acoustic arrays.

Image of FIG. 6.
FIG. 6.

The results of the wind field retrieval. (a) Upper left: original wind field. (b) Upper right: average results of the retrieval. (c) Lower left: difference between the retrieved and the original wave fields. The systematic error is due to confining the pseudo-inversion to the subspace formed by the leading 6 (out of 12) eigenfunctions. The systematic error is relatively low because the original wind field is similar in shape to the first eigenfunction. (d) Lower right: summary error which includes both systematic error and error due to finite integration time. The overall error is close to the systematic, due to masking effect of noise due to strong wind, which grows as (approximately) a cube of the wind speed.

Image of FIG. 7.
FIG. 7.

Same as Fig. 6, but with the original wind field far from the subspace formed by the first six eigenfunctions.


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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Mapping of the ocean surface wind by ocean acoustic interferometers