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Willard J. Pierson Jr.

Hasselmann et al.(1976) that relate to the efforts described by Piersonet al. (1966). For one thing, the model we describedthen is now being used operationally for the NorthernHemisphere oceans at the Fleet Numerical WeatherCentral in Monterey, Calif., with fairly satisfactoryresults. Moreover, a model that includes the effectsof wave refraction for forecasting waves in hurricaneshas been developed and is being tested at the NOAAAtlantic Oceanographic and Meteorological Laboratory against measurements of

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Jordi Isern-Fontanet, Mahesh Shinde, and Cristina González-Haro

average is taken over those wavevectors with the same modulus. On the other side, the phase shift can be obtained as Equivalently, a transfer function F T ( k ) and phase shift can be defined for the SST anomaly . Then, Eq. (9) becomes Notice that, if salinity would not contribute to surface buoyancy variability then, F T ( k ) ∝ F b ( k ) and . 3. Numerical simulations a. The Mediterranean Forecasting System In this study, we used the nowcasts provided by the Mediterranean Forecasting System

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J. O. S. Alves, K. Haines, and D. L. T. Anderson

reported on here. These results confirm that the positive impacts of the scheme here described are carried over into seasonal prediction. Some work has also been done to investigate the potential benefit of sea level data for simulating and predicting ENSO. Fischer et al. (1995) used a statistical technique to assimilate pseudo sea level data from an NCEP (National Center for Environmental Prediction) analyses ( Ji and Leetmaa 1997 ; Ji et al. 1995 ). They produced ENSO forecasts from these analyses

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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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K. Hasselmann, D. B. Ross, P. Müller, and W. Sell

134 JOURNAL OF PHYSICAL OCEANOGRAPHY Vouu~7COMMERCE 5-35292 and 04-6-158-44049 and bythe Naval Air Systems Command under ContractN000 19-76-C-0310. Contribution Number 80, CUNYInstitute of Marine and Atmospheric Sciences. REFERENCESCardone, V. J., 1969: Spedfication of the wind field distribution in the marine boundary layer for wave forecasting. Rep. TR 69-1, Geophys. Sd. Lab

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William R. Holland and Paola Malanotte-Rizzoli

no errors;and (iii) the data are dynamically compatible with the model since they are simulated by the model itself in acontrol run. We reach the following conclusions. In principle, assimilation of altimetric data with a simple relaxation("nudging") technique can be very successful in driying the assimilation model to the control run even in thedeep layers for which no data are supplied. This is achieved with a "nearly perfect" space-time resolutionsurface height dataset in which data are

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Fabrice Veron, W. Kendall Melville, and Luc Lenain

relatively recently ( Hsu et al. 1981 ; Snyder et al. 1981 ; Rapp and Melville 1990 ; Komen et al. 1994 ). It is now accepted that ocean surface waves may support much of the momentum transfer from the atmosphere to the ocean. For example, following extensive studies of the role of waves on the air–sea momentum flux, the European Center for Medium-Range Weather Forecasting incorporated wave effects in their momentum flux estimates ( Komen et al. 1994 ). More recently still, the influence of the

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G. Ph van Vledder and L. H. Holthuijsen

with numerical simulations oftbe physical processes involved. The observationswere acquired with pitch-and-roll buoys in the central and southern North Sea. They are selected and correctedto represent locally generated homogeneous wave fields in deep water. The response time scales thus obtainedagree well with one published dataset. The disagreement with other published datasets is shown to be due todifferences in analysis techniques, at least partially. The numerical simulations are carried out

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

, two-dimensional joint probabilities) have to be estimated. These can be interpreted in terms of the time transition probabilities or as the dynamical contribution to the component-wise Frobenius Perron operator of the underlying dynamical system. Minimizing 〈( ẋ j − Σ i =1 Φ i j ( x i )) 2 〉 is equivalent to the maximization of the correlation where it is assumed that all variables have zero mean. Hence, this technique to solve the nonlinear regression problem is also called maximal

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Cortis Cooper and Bryan Pearce

deemed most appropriate for the two hindcasts. This latter finding suggests that including waveeffects in the bottom-boundary-layer dynamics can improve current forecasts and hindcasts with the circulation model by a factor of more than 2 in shallower waters. The findings are also relevant to the majorityof finite-depth circulation models.1. Introduction Reasonably high-quality data sets taken duringTropical Storm Delia and Hurricane Anita are available in the literature and offer an 6pportunity 1

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