Search Results

You are looking at 141 - 150 of 962 items for :

  • Regression analysis x
  • Journal of Physical Oceanography x
  • Refine by Access: All Content x
Clear All
M. A. Merrifield

shelf wave.These wave events are dissipated somehow in the Gulfof California, as CDG do not detect them in tide gaugeobservations on the Pacific Ocean side of the Baja California peninsula. Enfield and Allen use cross-spectra,case history, and multiple regression analysis to showthat these waves are generated by strong coastal windsassociated with tropical storms and hurricanes thattypically occur from May to October off the Pacificcoast of Mexico between lO°-20°N. In this region

Full access
Peter K. Taylor and Margaret J. Yelland

) implies smaller corrections to the ID results but would still introduce an appreciable wave-age-dependent signal of opposite sign to that normally expected. In the analysis summarized in Fig. 6 , it was necessary to represent wave age by c p / U 10 n to minimize the effects of spurious correlations. However, similar results are obtained by analysis of the data by calculating anomalies from a regression of C D 10 n on u ∗, and then using c p / u ∗ to represent wave age. In this case, the

Full access
Dong-Ping Wang, Lie-Yauw Oey, Tal Ezer, and Peter Hamilton

modes of observed currents are almost identical to the first two modes of observed currents of the SVD analysis. We can take advantage of the larger spatial coverage of satellite SLA to associate the eddy features to the open ocean influence. We calculate the regression of SLA time series at each grid point with the first and second SVD modes of observed currents. Figure 12 shows the correlation coefficient maps for the first and second modes. The mode-1 correlation map indicates a single eddy

Full access
Edgar L. Andreas

example, I review the general problem of fictitious correlation and how to mathematically evaluate its potential effect. I do this by developing equations that predict the best fits for C DN, λ p /2 and k p z 0 as functions of ω * under the assumption that none of the measured variables are correlated. Lines fitted to scatterplots that are based on this analysis are little different from lines based on the actual data. Randomly scrambling the k p values in the DMAJ dataset reiterates the

Full access
R. T. Guza, E. B. Thornton, and N. Christensen Jr.

associated currents arequalitatively discussed. In section 4, an empirical orthogonal eigenfunction (EOF) analysis is used to describe the spatial and temporal patterns of longshorecurrent variability. The first EOF has a classic, parabolicspatial structure. In section 5, the temporal variabilityof the first EOF is shown to be correlated equallystrongly with Sr~ and with a scale velocity suggestedby radiation stress-based longshore current theories.The total radiation stress, and not the structure ofS

Full access
Guillermo Gutiérrez de Velasco and Clinton D. Winant

, located 10 km north of El Remate. Fluctuations in wind speed and direction are highly correlated between these sites, but the analysis presented here suggests that the San Angel observations somewhat underestimate the strength of the wind blowing over the lagoon. c. Surveys Seasonal hydrographic surveys were conducted during each field trip, during both spring and neap tides. A Sea-Bird Electronics, Inc., SBE-19 conductivity, temperature, and depth (CTD) sensor was cast from a small boat at about 50

Full access
C. A. Hegermiller, J. A. A. Antolinez, A. Rueda, P. Camus, J. Perez, L. H. Erikson, P. L. Barnard, and F. J. Mendez

multivariate linear regression analysis to assess the skill of the predictor to reproduce the predictand (daily wave conditions for each family). The methodology is applied to a location in deep water off the coast of Southern California, United States (33°N, 120°W), as a demonstrative tool. Fig . 1. Flowchart of the general methodology to define the optimal predictor for statistical downscaling of multimodal wave climate. Improvements to the Camus et al. (2014a) definition of the predictor are

Full access
Kazuya Kusahara and Kay I. Ohshima

offset of −15 cm. Fig . 6. Maps of (a) correlation and (b) regression coefficient of sea level with the leading mode of the coastal sea level for periods from 10 to 200 days in the model: Contour interval is 0.1 in both panels. The dotted lines indicate negative values. The 3000-m depth contours are denoted by thick dashed lines. Fig . 7. Spatial distribution of the amplitude and phase at the coast around Antarctica from CEOF analysis of the sea level in the model: See the text for the specific

Full access
Allan J. Clarke, Jianguo Wang, and Stephen Van Gorder

.1) with Δ replaced by [1 + x ( t )/ c ]. The Δ = const analysis of section 3a(1) still applies, so oscillations are still possible, but since the delay is so small, values of a and b have to be very finely tuned to give ENSO frequency solutions. However, in reality at least one of the other delayed negative feedback terms in (2.8) should not be ignored. Because the delay [1 + x ( t )/ c ] months is small compared with the delays Δ [see (2.5) ] or δ [see (2.7) ] and because t − 1 − x

Full access
R. W. Lindsay and J. Zhang

determine causes of the changes and in order to design efficient monitoring systems to track them. One technique to determine the major modes of variability of the ice extent or thickness is through the use of empirical orthogonal functions (EOFs). An EOF analysis of the weekly ice concentration as represented in the National Ice Center (NIC) ice charts from the period 1972–94 was made by Partington et al. (2003) . They report that the major winter mode is a dipole of ice concentration between the

Full access