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Rafael C. Gonçalves, Mohamed Iskandarani, Tamay Özgökmen, and W. Carlisle Thacker

drifters and inferring the derived quantities. All information is gleamed from the drifter data, without recourse to outside sources. The technique applied here is Gaussian Process Regression (GPR), which has wide application in geostatistics and problems of forward propagation of uncertainty in numerical models ( Kennedy and O’Hagan 2000 ; Rasmussen and Williams 2006 ; Thacker et al. 2015 ; Iskandarani et al. 2016 ). The analysis is centered on one specific event when 326 drifters were released

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Eric W. Leuliette and John M. Wahr

to produce the thermal expansion seen the modes. To determine this coefficient we have used the method of weighted least squares analysis, a “robust” regression, because there are regions in each basin where thermal expansion is clearly not the dominant process and they should be treated as outliers in the regression. The method of the least median of squares deviation (LMS) was used to determine the weightings for a least squares fit ( Rousseeuw 1984 ; Rousseeuw and Leroy 1987 ). The least

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Malcolm E. Scully

loading. A linear regression analysis is performed on the time series of the observed hypoxic volumes to examine the long-term trends in the data ( Table 2 ). To normalize the results, each variable considered in the regression is first divided by its standard deviation. Consistent with previous findings ( Hagy et al. 2004 ), estimated nitrogen loading explains a relatively small fraction of the observed variance in hypoxic volume ( Fig. 1a ). Moreover, when only nitrogen loading is considered, there

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A. Timmermann, H. U. Voss, and R. Pasmanter

our case. In the prediction context only tendency errors of the variables x i have to be minimized. No physical interpretation of the components is required. In our situation one is faced with the problem of selecting the “correct” functional forms for the global model out of an infinite set of functions. The combination of nonparametric regression analysis and the maximal correlation method provides some objective means for extracting the optimal functionals. Furthermore, we impose restrictions

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Pedro N. DiNezio, Lewis J. Gramer, William E. Johns, Christopher S. Meinen, and Molly O. Baringer

NAO and the WSC field over the latitude band of the Straits of Florida is further explored in the next section using a regression analysis of the wind fields and the low-frequency NAO index. 3. Results a. WSC forcing associated with NAO interannual variability Since the NAO is a major mode of interannual atmospheric variability over the North Atlantic, we examine low-frequency covariability between the NAO and the wind field over the forcing regions at 27°N. We linearly regress the monthly wind

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Joyce E. Scemitz and Andrew C. Vastano

1967 observation of a ring by the Woods Hole Oceanographic Institution provided estimates of the derivatives of thetemperature. Regression analysis was used to determine the coefficients for polynomiM representations of~ (r,z) for selected combinations of Kn and K,. The study indicates upper bounds on the order of magnitudesfor the diffusivities (Kh,K~)= (10~,10) cm~ s-t based upon near-minimum least-squares error estimates fromthe regression analysis. An important result is that little

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Christopher G. Piecuch, Ichiro Fukumori, and Rui M. Ponte

. In section 3 , we establish the horizontal scales and vertical structure of the dominant intraseasonal sea level variation in the Persian Gulf. In section 4 , we use dynamical theory, linear regression, and correlation analysis to identify the main local and nonlocal forcing mechanisms and ocean dynamics responsible for driving intraseasonal variations in Persian Gulf sea level and their relation to large-scale circulation and climate in the equatorial and north Indian Ocean. We conclude with a

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Gengxin Chen, Dongxiao Wang, Weiqing Han, Ming Feng, Fan Wang, Yuanlong Li, Ju Chen, and Arnold L. Gordon

can be induced by the Pacific Ocean variability through the ITF. Fig . 6. Correlation coefficients between interannual SEC transport anomaly along section 110°E and the interannual SSH anomaly at each grid point from (a) BRAN, (b) HYCOM CR, and (c) HYCOM PAC, when SSH leads transport by 6, 4, 2, and 0 months. The regression analysis with ENSO and IOD indices, Tfit( t ) = −2.01 × IOD( t − 1) + 2.74 × ENSO( t − 7) + 0.35, reasonably predicts interannual variation of the SEC transport ( Fig. 5b

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David B. Enfield and J. S. Allen

1012 JOURNAL OF PHYSICAL OCEANOGRAPHY VOLUME 13The Generation and Propagation of Sea Level Variability Along the Pacific Coast of Mexico DAVID B. ENFIELD AND J. S. ALLENSchool of Oceanographj~. Oregon State University, Corvallis, 97331(Manuscript received 20 September 1982, in final form I March 1983) Case history analysis, cross spectra and multiple regression analysis have been us~! in a study

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David A. Griffen and Jason H. Middleton

~04 JOURNAL OF P~YSICAL OCEANOGRAPHY -OLUME21Local and Remote Wind Forcing of New South Wales Inner' Shelf Currents and Sea Level DAVID A. GRIFFIN AND JASON H. MIDDLETONMathematics Oceanography Laboratory, University of New South Wales, Kensington, NSW, Australia(Manuscript received ? May 1990, in final form 20 August 1990) Linear multiple regression analysis is used to identify the locally and remotely wind

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