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Miles G. McPhee

. We can then solvefor the exponent b in a least-squares sense usinglinear regression analysis on the logarithmic equation logvw = b logV + loga. From the sample population we can estimate b and calculate an intervalabout the estimate within which we are confident to agiven probability the actual value of b is contained. In a discussion of the form expected for thestress-velocity curves in very simple models (McPhee, 1977b), we have shown that b ranges fromunity in the case of the classical

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E. H. Schumann and K. H. Brink

measured.Thus, for a coordinate system where the y-axis pointsalong the coastline, with the x-axis offshore, geostrophyimplies: fo =g~x (3)where v is the y-component of velocity, g is the acceleration due to gravity, and f~/is the change in adjustedsea level over the length scale Geostrophy (3) implies a linear relationship betweenalongshore current variation v and changes in coastalsea level f~. A regression analysis was done between vand ~1, and

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Volker H. Strass

regression of therespective amplitudes. The scaling factors used by Fiekas et al. in their data analysis do not deviate muchfrom those obtained. For scaling the vorticity divergence the amplitude regression yielded a factor of 39m; Fiekas et al., and earlier Leach (1987), used a depthscale of 50 m. For the convergence of Q vectors noscaling has been used in the companion paper or inLeach (1987), but instead the Q-vector potential wasintroduced to obtain an estimate of vertical velocityfrom the Q

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Nils H. Rix and Jürgen Willebrand

and of lateral mixingand formulated a simple inverse problem to estimatethe respective parameters. Again, the noise level prevented an accurate determination of both parameters,and no information on their spatial or temporal structure could be obtained. Therefore, only the results ofthe regression analysis are presented.5. Summary and conclusions We have investigated the relationship between thehorizontal component of the bolus velocity and its parameterization according to GM90 from data of a

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M. L. Heron

assumed to be of the form G(O) = A cos2S(o/2) (2)where 0 is the angle measured from the wind direction.Although this form is empirical it has found widespreadusage both in wave buoy analysis (e.g., Longuet-Higginset al., 1963; Mitsuyasu et al., 1975; Hasselmann et al.,1980) and in HF radar analysis. An unfortunate notation difference has occurredwhereby Longuet-Higgins et al. (1963) use cosS(O/2)for the waveheight distribution but Tyler et al. (1974)use cosS(O/2) for

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Frank Kauker and Hans von Storch

function—require costly in situ operations. Because of this limitation, the international oceanographic community has embarked on the challenging undertaking of “operational oceanography,” which by means of intelligent merging of dynamical understanding (i.e., quasi-realistic models), of educated guessing (i.e., routine forecasts), and routine in situ and remotely sensed observations of a wide range of variables allows for a instantaneous, synoptic analysis of the state of the ocean ( Robinson et al

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Amandine Schaeffer, Moninya Roughan, and Bradley D. Morris

the barotropic pressure gradient term computed from satellite observations (see section 6 ). Nevertheless, it is not expected to have a significant impact on the results of this study (see section 4c ). c. Correlation and regression analysis The dominant geostrophic equilibrium at all locations is evident when comparing the temporal variability and the magnitude of different terms of the momentum balance ( Table 4 ). Indeed, both the highest correlation and regression coefficients are obtained

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Hongyang Lin, Keith R. Thompson, and Jianyu Hu

analysis of the western boundary transport from GLORYS, 1993–2009. (a) The dominant eigenvector (red arrows). (b) The first principal component (red; ) and normalized sea level at B1(black; for location see Fig. 1b ). The black contours in (a) show the regression coefficient from the regression of streamfunction (calculated from the GLORYS transports) on . The contour interval is 5, with zero contours shown by heavy lines and positive (negative) contour values shown by thin solid (dashed) lines

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C. Smyth and Alex E. Hay

some discrepancies. There is also good agreement between relative roughness estimates and extrapolated results from Madsen et al. (1990) , which suggests that their roughness estimate [ Eq. (12) ] is valid over a wider range of wave energies. A new expression for the bed roughness was derived through regression analysis of measured friction factors and three length scales: A, η, and λ. A reasonably good predictor of f w for all bed states appears to be f w ∼ A −2.5 . Selecting the

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Warren B. White and J. F. T. Saur

's analysis indicated that, in the wake ofthe polev~ard moving baroclinic Kelvin-waves, baroclinic long waves propagate westward into the interiorocean. Price and Magaard (1980) have observed interannual baroclinic long waves in the interior ocean,but it remains to be seen whether they originatedfrom the coast, or' were generated locally. In thisstudy, interannual baroclinic long waves south of30-N were found to have been associated with coastalsea level variability, but north of 30-N, they appearto have

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