Search Results

You are looking at 51 - 60 of 957 items for :

  • Regression analysis x
  • Journal of Physical Oceanography x
  • Refine by Access: All Content x
Clear All
Peter C. Mcintosh and Richard B. Schahinger

obtained from theWLS analysis (not shown in the Table), although thereis no significant correlation at the shallowest 1200-minstrument. The two methods also give similar results if we useas our measure of fit the linear regression coefficient,/3, between the observed and predicted data rather thanthe correlation coefficient. (Here ~ is defined as thatnumber that when multiplied by the observed timeseries gives the best fit to the predicted time series inthe least-squares sense; see Draper and Smith

Full access
Brady S. Ferster, Bulusu Subrahmanyam, Ichiro Fukumori, and Ebenezer S. Nyadjro

transport is estimated across 30°S and binned based on 27.72 and 28.11 kg m −3 neutral-density surfaces. Table 2. Statistics from Fig. 5 ; the time-mean and regression of the density-integrated climatological anomalies in meridional volume transport from 1992 to 2015 in ECCO V4r3. An alpha of 0.05 is used to determine significance. The temporal analysis of the meridional branches of the overturning cells’ transports is used to explore the strength of the global overturning circulation. Within the

Full access
Julien Jouanno, Frédéric Marin, Yves du Penhoat, and Jean-Marc Molines

meridional velocity by a few days. Marin et al. (2009) also concluded that the undulations of the thermal front in the Gulf of Guinea were in phase with wind intensification. From a regression analysis of satellite observations, de Coëtlogon et al. (2010) found a 5-day lag between the wind peaks and cold SST anomalies in the 10–20-day band of frequency. This variability contrasts with the western part of the basin where the intraseasonal modulation of the SST is dominated by 25–40-day fluctuations

Full access
Jianke Li and Allan J. Clarke

function ( Fig. 3a ) is representative of sea level amplitude. The dominance of a single EOF over more than 6000 km of coastline shows that the sea level is in phase all along the coast. This in-phase relationship is confirmed by a lagged regression analysis of the nine sea level stations, which showed that the maximum correlation between stations was at zero lag. At first sight this zero lag result seems to contradict theory because, as mentioned earlier, along Australia's nearly zonal southern coast

Full access
Jae-Yul Yun, Kyung-Il Chang, Kwang-Yul Kim, Yang-Ki Cho, Kyung-Ae Park, and Lorenz Magaard

evolution of spatial patterns over the period d ; P n ( t ) shows the time-varying strength of the physical evolution. Relationship between two variables, for example, Niño-3 as a target variable and SST as a predictor variable, is obtained by regression analysis. After the SSTs are decomposed into CSEOF modes, the corresponding PCTs P n ( t )| predictor , are regressed onto Niño-3: where P 1 ( t )| target is Niño-3, a n is the regression coefficient, and ε ( t ) is the regression error. The

Full access
G. Ph van Vledder and L. H. Holthuijsen

(a) + b log(e,) has beenapplied. For each set the coefficient of linear correlationhas been computed, as well as the estimated standarddeviation in the regression coefficients; see Table 2.The results of the regression analysis indicate an increasing correlation as the relative error is reduced, except for the case where a,/7 < 0.5. From a subjectiveassessment of the standard deviations, the number of 10+73', 10+s10+s (a) all selected__-.. .. .10*4 .004.01 .0210 +73'$10*e (b

Full access
Robin Waldman, Joël Hirschi, Aurore Voldoire, Christophe Cassou, and Rym Msadek

. The northward transport occurs predominantly at the western boundary from 15°S to 40°N and in the interior ocean outside those latitudes. Bottom velocities are also intensified along western boundaries and in most of the subpolar North Atlantic ( Fig. 2c ), illustrating the external-mode contribution to the mean AMOC transport. Fig . 2. (a) Mean Atlantic meridional overturning streamfunction Ψ (color shading) and its regression onto the leading multidecadal principal component (contours) in CNRM

Open access
Jianke Li and Allan J. Clarke

1. Introduction Interannual variations in the equatorial Pacific Ocean waveguide (5°N–5°S) have been the focus of research in the Pacific for the last several decades because it is there that the El Niño–Southern Oscillation (ENSO), the world’s leading short-term climate fluctuation, is generated. Comparatively little is known about the vast tropical South Pacific south of 5°S. However, now that near-global satellite altimeter measurements have been available for more than a decade, analysis of

Full access
Peter Müller and Angelika Lippert

’s function, as in (2.1) , then one can perform a linear multiple regression analysis in order to estimate the Green’s function. The best estimate for the Green’s function, in a least squares sense, is then given by (3.1) ( Bendat and Piersol 1971 ) where now C pF is the observed cross-covariance map, C FF the observed autocovariance function of the wind field, and G the Green’s function to be estimated. The task is thus to invert, or deconvolute, the expression (3.1) to obtain the Green

Full access
Anthony R. Kirincich, Steven J. Lentz, and John A. Barth

-shelf exchange. If present, this wave-driven return flow would be superimposed on across-shelf circulation due to wind or pressure forcing, complicating the analysis of across-shelf exchange observations. In a companion study to Lentz et al. (2008) , M. Fewings et al. (2008) found that wave-driven circulation had a profound effect on observations of across-shelf exchange due to wind forcing, altering the measured vertical structure of across-shelf velocity during all but the smallest wave climates. A

Full access