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Weiqi Lin, Lawrence P. Sanford, Steven E. Suttles, and Richard Valigura

) and α = 0.018 by Wu (1980) . Most of the field experiments over the past have established a statistically significant dependence of C d on U n 10 . The general form of this linear regression can be expressed as C d = ( a + bU n 10 ) × 10 −3 , (7) where a and b are coefficients determined by the data. In MARSEN, a = 8.47 × 10 −2 and b = 0.577 ( Geernaert et al. 1987 ); in HEXMAX, a = 9.1 × 10 −2 and b = 0.50 ( Smith et al. 1992 ); in RASEX, a = 6.7 × 10 −2 and b = 0

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Allan J. Clarke and Rizwan Ahmed

data were only of a few hours and these were treated as suggested by G. T. Mitchum (1987, personal communication). When gaps occur, they are given zero weight in the filter weighting function and the total weighting is appropriately renormalized. In this way, sometimes gaps in the time series could be filled in. A criterion is available for determining when the interpolated point is likely to be inaccurate so that the point can be flagged as missing. Based on Mitchum’s analysis, an interpolated

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C. Herbaut, J. Sirven, and S. Février

. This heat flux anomaly is negative over the subpolar gyre and positive over the subtropical gyre, and its integral over the basin is null ( Fig. 4c ). Note that the time series is the same as in W st . The analysis follows that of W st . The SST of experiment Q st has been regressed onto the second PC of T 300 of Q st ( Fig. 11 ). The regression shows SST anomalies propagating from the intergyre region into the subpolar basin, suggesting a dynamics similar to that observed in W st (cf. Fig. 7

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Hiroto Abe, Youichi Tanimoto, Takuya Hasegawa, and Naoto Ebuchi

To extract the ENSO-related atmospheric and oceanic components, we used a regression analysis by relating these variables to the Niño-3.4 index. Statistical significance of the regression coefficients was evaluated based on correlation coefficient between each variable and Niño-3.4 index. The degree of freedom (DOF) was calculated by dividing the number of data by the zero-crossing time scale. The DOF of each variable and that of Niño-3.4 index were averaged to determine single value of DOF. To

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Diane Masson

~(f) is the estimated mean direction for a spectral band centered around frequency f, OH is the winddirection, and w = 2~rfis the angular frequency. Froma one parameter regression analysis, they obtained anaverage relaxation coefficient B = 2.0 x 10-s. Allenderet al. (1983) extended the above relationship to includea wind speed dependency by including the extra factorU/c(f) in (2), with Uthe wind speed at a 10 m height,and c(f) the wave phase speed. In both studies, themeasured directional

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Rachel Horwitz and Steven J. Lentz

form of Regression slope A is the ratio of two values with units of transport, so it represents a nondimensional transport. A multiple regression analysis including cross-shelf wind stress, along-shelf wind stress, and wave forcing yields similar regression coefficients for in Eqs. (5) and (6) . 4. Inner-shelf circulation a. Width of inner shelf Following Tilburg (2003) , for a purely cross-shelf wind stress we define the inner shelf as the region onshore of where the surface boundary layer

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Chris Garrett, John Akerley, and Keith Thompson

analysis will pick out notonly the true tidal modulation but also a contributionfrom the unrelated background spectra at 15 and 28day periods multiplied by a narrow bandwidth inverselyproportional to the length of the record. Allowing forthis, the regression coefficients become respectively-0.067 + 0.042 and 0.060 _+ 0.092 at the 95% confidence level. Using the M2 values of aa and aT-o fromTable I the regressions imply mean tidal contributionsof-21 + 14 or 7 + 10 mm respectively, to ~c - ~or assuming

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M. G. Hadfield

lags. Results are presented here for a base month of November because that is the month showing the largest lagged correlations. The regression analysis addresses questions of the form: Given a certain SST anomaly in November, what is the best estimate (and associated uncertainty) of the anomaly n months later in quantity x ? Figure 6 shows the slope coefficients for SST and subsurface temperature regressed against November SST. For SST (upper panel) the estimated slope coefficients at 7

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E. D. Zaron

microscale and mesoscale temporal processes, respectively. Note the factor of ½ on the left-hand sides of (7) and (8) , which is included so that the model coefficients correspond to SSH variance associated with the given process. 3. Results Parameters in the above model are determined in a two-stage process. The first stage consists of neglecting the nonstationary tides and determining the , , and parameters by least squares regression of with the model . In the second stage of analysis, the

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Tom P. Rippeth, Eirwen Williams, and John H. Simpson

. A comparison of the along-channel stress estimated from single-ping data from a 25-cm bin with the estimate made using the 4-ping mean data and a 50-cm bin size is given in Fig. 3 . Regression analysis of the ∼12 h of data indicates that the stresses from the 4-ping data are underestimated by ∼23% relative to the single-ping results. We have therefore applied a correction of 1.30 to all our stress estimates. Most of this correction is attributable to the 4-ping averaging with only a relatively

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